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 "cells": [
  {
   "cell_type": "markdown",
   "id": "f8aabc3a-5418-48db-af11-03234664f785",
   "metadata": {},
   "source": [
    "# 第2章 基于Keras的神经网络在大宗商品采购中的应用"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de89ec7e",
   "metadata": {},
   "source": [
    "## 步骤1：数据预处理（在数据库/Excel中）\n",
    "数据预处理是指将数据按照所需特征进行转换。我们在编写Python代码之前就进行这一步，以减少所需要的代码层次（也就是说，我们先直接与SQLite交互，而不是到了Python那一步再与SQLite交互）。以下是执行数据库操作所涉及的步骤：\n",
    "\n",
    "1．创建SQLite数据库。\n",
    "\n",
    "2．将数据导入成临时表。\n",
    "\n",
    "3．创建所需的表——这是个一次性的操作。\n",
    "\n",
    "4．将临时表的数据经过数据类型和格式转换之后插入到实际表中。\n",
    "\n",
    "5．创建进行特征工程所需的视图。\n",
    "\n",
    "6．将预处理后的视图输出为CSV数据。\n",
    "\n",
    "**本步骤因为数据库部分，所以没有列出**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8acc4927",
   "metadata": {},
   "source": [
    "## 步骤2：导入库和定义变量\n",
    "\n",
    "**与原书配套源代码不一样的是，jupterlab版本将在下面的步骤中按需导入，而不是一开头就导入全部**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "146a1c32",
   "metadata": {},
   "source": [
    "## 步骤3：读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8440e7c4-1e13-41a4-a411-890ede4b771f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2dc3b693-6154-4acb-91fa-293442782112",
   "metadata": {},
   "outputs": [],
   "source": [
    "f_in_name = '天然气使用量.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "faae478b-b94e-48bd-9ad0-be88f95a2dac",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd_trade_history = pd.read_csv(f_in_name,header=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "55df1eb3-1aa7-4aa4-b41d-86888ac39037",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
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    "tags": []
   },
   "outputs": [
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       "      <td>53569.052</td>\n",
       "      <td>61761.267</td>\n",
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       "      <td>83676.968</td>\n",
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       "     t_p_1_mth_n  t_p_1_qtr_n      date_d      val_n  t_m_1_val_n  \\\n",
       "0              2          0.0  2002-01-01  53569.052    61761.267   \n",
       "1              3          1.0  2002-02-01  46029.894    53569.052   \n",
       "2              4          1.0  2002-03-01  62952.670    46029.894   \n",
       "3              5          1.0  2002-04-01  44073.768    62952.670   \n",
       "4              6          2.0  2002-05-01  44767.966    44073.768   \n",
       "..           ...          ...         ...        ...          ...   \n",
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       "188           10          3.0  2017-09-01  62609.118    78072.015   \n",
       "\n",
       "     t_m_2_val_n  t_m_3_val_n  t_m_4_val_n  t_m_5_val_n  t_m_6_val_n  \\\n",
       "0      59350.408    75445.413    83676.968   100006.244    92713.317   \n",
       "1      61761.267    59350.408    75445.413    83676.968   100006.244   \n",
       "2      53569.052    61761.267    59350.408    75445.413    83676.968   \n",
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       "4      62952.670    46029.894    53569.052    61761.267    59350.408   \n",
       "..           ...          ...          ...          ...          ...   \n",
       "184    31506.404    37508.317    51157.470    54108.365    48998.221   \n",
       "185    27416.917    31506.404    37508.317    51157.470    54108.365   \n",
       "186    35394.170    27416.917    31506.404    37508.317    51157.470   \n",
       "187    49345.909    35394.170    27416.917    31506.404    37508.317   \n",
       "188    67120.715    49345.909    35394.170    27416.917    31506.404   \n",
       "\n",
       "     t_m_7_val_n  t_m_8_val_n  t_m_9_val_n  t_m_10_val_n  t_m_11_val_n  \\\n",
       "0      84253.446    83703.337    80723.902     82935.579     78687.679   \n",
       "1      92713.317    84253.446    83703.337     80723.902     82935.579   \n",
       "2     100006.244    92713.317    84253.446     83703.337     80723.902   \n",
       "3      83676.968   100006.244    92713.317     84253.446     83703.337   \n",
       "4      75445.413    83676.968   100006.244     92713.317     84253.446   \n",
       "..           ...          ...          ...           ...           ...   \n",
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       "185    48998.221    63460.576    64331.206     78969.654     71836.554   \n",
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       "187    51157.470    54108.365    48998.221     63460.576     64331.206   \n",
       "188    37508.317    51157.470    54108.365     48998.221     63460.576   \n",
       "\n",
       "     t_m_12_val_n  t_p_1_val_n  t_p_2_val_n  t_p_3_val_n  t_p_4_val_n  \n",
       "0       90069.515    46029.894    62952.670    44073.768    44767.966  \n",
       "1       78687.679    62952.670    44073.768    44767.966    64112.377  \n",
       "2       82935.579    44073.768    44767.966    64112.377    88478.110  \n",
       "3       80723.902    44767.966    64112.377    88478.110    80440.587  \n",
       "4       83703.337    64112.377    88478.110    80440.587    75297.618  \n",
       "..            ...          ...          ...          ...          ...  \n",
       "184     39061.798    49345.909    67120.715    78072.015    62609.118  \n",
       "185     61451.699    67120.715    78072.015    62609.118    58492.030  \n",
       "186     71836.554    78072.015    62609.118    58492.030    48013.183  \n",
       "187     78969.654    62609.118    58492.030    48013.183    50534.526  \n",
       "188     64331.206    58492.030    48013.183    50534.526    45594.032  \n",
       "\n",
       "[189 rows x 20 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd_trade_history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5bc7ce74-e070-44a2-95b9-f253f90862f5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "pd_trade_history = pd_trade_history.drop('date_d',1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1b811e7f-6572-46cf-9bfd-539f801ee638",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>t_p_1_mth_n</th>\n",
       "      <th>t_p_1_qtr_n</th>\n",
       "      <th>val_n</th>\n",
       "      <th>t_m_1_val_n</th>\n",
       "      <th>t_m_2_val_n</th>\n",
       "      <th>t_m_3_val_n</th>\n",
       "      <th>t_m_4_val_n</th>\n",
       "      <th>t_m_5_val_n</th>\n",
       "      <th>t_m_6_val_n</th>\n",
       "      <th>t_m_7_val_n</th>\n",
       "      <th>t_m_8_val_n</th>\n",
       "      <th>t_m_9_val_n</th>\n",
       "      <th>t_m_10_val_n</th>\n",
       "      <th>t_m_11_val_n</th>\n",
       "      <th>t_m_12_val_n</th>\n",
       "      <th>t_p_1_val_n</th>\n",
       "      <th>t_p_2_val_n</th>\n",
       "      <th>t_p_3_val_n</th>\n",
       "      <th>t_p_4_val_n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53569.052</td>\n",
       "      <td>61761.267</td>\n",
       "      <td>59350.408</td>\n",
       "      <td>75445.413</td>\n",
       "      <td>83676.968</td>\n",
       "      <td>100006.244</td>\n",
       "      <td>92713.317</td>\n",
       "      <td>84253.446</td>\n",
       "      <td>83703.337</td>\n",
       "      <td>80723.902</td>\n",
       "      <td>82935.579</td>\n",
       "      <td>78687.679</td>\n",
       "      <td>90069.515</td>\n",
       "      <td>46029.894</td>\n",
       "      <td>62952.670</td>\n",
       "      <td>44073.768</td>\n",
       "      <td>44767.966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>46029.894</td>\n",
       "      <td>53569.052</td>\n",
       "      <td>61761.267</td>\n",
       "      <td>59350.408</td>\n",
       "      <td>75445.413</td>\n",
       "      <td>83676.968</td>\n",
       "      <td>100006.244</td>\n",
       "      <td>92713.317</td>\n",
       "      <td>84253.446</td>\n",
       "      <td>83703.337</td>\n",
       "      <td>80723.902</td>\n",
       "      <td>82935.579</td>\n",
       "      <td>78687.679</td>\n",
       "      <td>62952.670</td>\n",
       "      <td>44073.768</td>\n",
       "      <td>44767.966</td>\n",
       "      <td>64112.377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>62952.670</td>\n",
       "      <td>46029.894</td>\n",
       "      <td>53569.052</td>\n",
       "      <td>61761.267</td>\n",
       "      <td>59350.408</td>\n",
       "      <td>75445.413</td>\n",
       "      <td>83676.968</td>\n",
       "      <td>100006.244</td>\n",
       "      <td>92713.317</td>\n",
       "      <td>84253.446</td>\n",
       "      <td>83703.337</td>\n",
       "      <td>80723.902</td>\n",
       "      <td>82935.579</td>\n",
       "      <td>44073.768</td>\n",
       "      <td>44767.966</td>\n",
       "      <td>64112.377</td>\n",
       "      <td>88478.110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>44073.768</td>\n",
       "      <td>62952.670</td>\n",
       "      <td>46029.894</td>\n",
       "      <td>53569.052</td>\n",
       "      <td>61761.267</td>\n",
       "      <td>59350.408</td>\n",
       "      <td>75445.413</td>\n",
       "      <td>83676.968</td>\n",
       "      <td>100006.244</td>\n",
       "      <td>92713.317</td>\n",
       "      <td>84253.446</td>\n",
       "      <td>83703.337</td>\n",
       "      <td>80723.902</td>\n",
       "      <td>44767.966</td>\n",
       "      <td>64112.377</td>\n",
       "      <td>88478.110</td>\n",
       "      <td>80440.587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>44767.966</td>\n",
       "      <td>44073.768</td>\n",
       "      <td>62952.670</td>\n",
       "      <td>46029.894</td>\n",
       "      <td>53569.052</td>\n",
       "      <td>61761.267</td>\n",
       "      <td>59350.408</td>\n",
       "      <td>75445.413</td>\n",
       "      <td>83676.968</td>\n",
       "      <td>100006.244</td>\n",
       "      <td>92713.317</td>\n",
       "      <td>84253.446</td>\n",
       "      <td>83703.337</td>\n",
       "      <td>64112.377</td>\n",
       "      <td>88478.110</td>\n",
       "      <td>80440.587</td>\n",
       "      <td>75297.618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>35394.170</td>\n",
       "      <td>27416.917</td>\n",
       "      <td>31506.404</td>\n",
       "      <td>37508.317</td>\n",
       "      <td>51157.470</td>\n",
       "      <td>54108.365</td>\n",
       "      <td>48998.221</td>\n",
       "      <td>63460.576</td>\n",
       "      <td>64331.206</td>\n",
       "      <td>78969.654</td>\n",
       "      <td>71836.554</td>\n",
       "      <td>61451.699</td>\n",
       "      <td>39061.798</td>\n",
       "      <td>49345.909</td>\n",
       "      <td>67120.715</td>\n",
       "      <td>78072.015</td>\n",
       "      <td>62609.118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>49345.909</td>\n",
       "      <td>35394.170</td>\n",
       "      <td>27416.917</td>\n",
       "      <td>31506.404</td>\n",
       "      <td>37508.317</td>\n",
       "      <td>51157.470</td>\n",
       "      <td>54108.365</td>\n",
       "      <td>48998.221</td>\n",
       "      <td>63460.576</td>\n",
       "      <td>64331.206</td>\n",
       "      <td>78969.654</td>\n",
       "      <td>71836.554</td>\n",
       "      <td>61451.699</td>\n",
       "      <td>67120.715</td>\n",
       "      <td>78072.015</td>\n",
       "      <td>62609.118</td>\n",
       "      <td>58492.030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>67120.715</td>\n",
       "      <td>49345.909</td>\n",
       "      <td>35394.170</td>\n",
       "      <td>27416.917</td>\n",
       "      <td>31506.404</td>\n",
       "      <td>37508.317</td>\n",
       "      <td>51157.470</td>\n",
       "      <td>54108.365</td>\n",
       "      <td>48998.221</td>\n",
       "      <td>63460.576</td>\n",
       "      <td>64331.206</td>\n",
       "      <td>78969.654</td>\n",
       "      <td>71836.554</td>\n",
       "      <td>78072.015</td>\n",
       "      <td>62609.118</td>\n",
       "      <td>58492.030</td>\n",
       "      <td>48013.183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>78072.015</td>\n",
       "      <td>67120.715</td>\n",
       "      <td>49345.909</td>\n",
       "      <td>35394.170</td>\n",
       "      <td>27416.917</td>\n",
       "      <td>31506.404</td>\n",
       "      <td>37508.317</td>\n",
       "      <td>51157.470</td>\n",
       "      <td>54108.365</td>\n",
       "      <td>48998.221</td>\n",
       "      <td>63460.576</td>\n",
       "      <td>64331.206</td>\n",
       "      <td>78969.654</td>\n",
       "      <td>62609.118</td>\n",
       "      <td>58492.030</td>\n",
       "      <td>48013.183</td>\n",
       "      <td>50534.526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>10</td>\n",
       "      <td>3.0</td>\n",
       "      <td>62609.118</td>\n",
       "      <td>78072.015</td>\n",
       "      <td>67120.715</td>\n",
       "      <td>49345.909</td>\n",
       "      <td>35394.170</td>\n",
       "      <td>27416.917</td>\n",
       "      <td>31506.404</td>\n",
       "      <td>37508.317</td>\n",
       "      <td>51157.470</td>\n",
       "      <td>54108.365</td>\n",
       "      <td>48998.221</td>\n",
       "      <td>63460.576</td>\n",
       "      <td>64331.206</td>\n",
       "      <td>58492.030</td>\n",
       "      <td>48013.183</td>\n",
       "      <td>50534.526</td>\n",
       "      <td>45594.032</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>189 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     t_p_1_mth_n  t_p_1_qtr_n      val_n  t_m_1_val_n  t_m_2_val_n  \\\n",
       "0              2          0.0  53569.052    61761.267    59350.408   \n",
       "1              3          1.0  46029.894    53569.052    61761.267   \n",
       "2              4          1.0  62952.670    46029.894    53569.052   \n",
       "3              5          1.0  44073.768    62952.670    46029.894   \n",
       "4              6          2.0  44767.966    44073.768    62952.670   \n",
       "..           ...          ...        ...          ...          ...   \n",
       "184            6          2.0  35394.170    27416.917    31506.404   \n",
       "185            7          2.0  49345.909    35394.170    27416.917   \n",
       "186            8          2.0  67120.715    49345.909    35394.170   \n",
       "187            9          3.0  78072.015    67120.715    49345.909   \n",
       "188           10          3.0  62609.118    78072.015    67120.715   \n",
       "\n",
       "     t_m_3_val_n  t_m_4_val_n  t_m_5_val_n  t_m_6_val_n  t_m_7_val_n  \\\n",
       "0      75445.413    83676.968   100006.244    92713.317    84253.446   \n",
       "1      59350.408    75445.413    83676.968   100006.244    92713.317   \n",
       "2      61761.267    59350.408    75445.413    83676.968   100006.244   \n",
       "3      53569.052    61761.267    59350.408    75445.413    83676.968   \n",
       "4      46029.894    53569.052    61761.267    59350.408    75445.413   \n",
       "..           ...          ...          ...          ...          ...   \n",
       "184    37508.317    51157.470    54108.365    48998.221    63460.576   \n",
       "185    31506.404    37508.317    51157.470    54108.365    48998.221   \n",
       "186    27416.917    31506.404    37508.317    51157.470    54108.365   \n",
       "187    35394.170    27416.917    31506.404    37508.317    51157.470   \n",
       "188    49345.909    35394.170    27416.917    31506.404    37508.317   \n",
       "\n",
       "     t_m_8_val_n  t_m_9_val_n  t_m_10_val_n  t_m_11_val_n  t_m_12_val_n  \\\n",
       "0      83703.337    80723.902     82935.579     78687.679     90069.515   \n",
       "1      84253.446    83703.337     80723.902     82935.579     78687.679   \n",
       "2      92713.317    84253.446     83703.337     80723.902     82935.579   \n",
       "3     100006.244    92713.317     84253.446     83703.337     80723.902   \n",
       "4      83676.968   100006.244     92713.317     84253.446     83703.337   \n",
       "..           ...          ...           ...           ...           ...   \n",
       "184    64331.206    78969.654     71836.554     61451.699     39061.798   \n",
       "185    63460.576    64331.206     78969.654     71836.554     61451.699   \n",
       "186    48998.221    63460.576     64331.206     78969.654     71836.554   \n",
       "187    54108.365    48998.221     63460.576     64331.206     78969.654   \n",
       "188    51157.470    54108.365     48998.221     63460.576     64331.206   \n",
       "\n",
       "     t_p_1_val_n  t_p_2_val_n  t_p_3_val_n  t_p_4_val_n  \n",
       "0      46029.894    62952.670    44073.768    44767.966  \n",
       "1      62952.670    44073.768    44767.966    64112.377  \n",
       "2      44073.768    44767.966    64112.377    88478.110  \n",
       "3      44767.966    64112.377    88478.110    80440.587  \n",
       "4      64112.377    88478.110    80440.587    75297.618  \n",
       "..           ...          ...          ...          ...  \n",
       "184    49345.909    67120.715    78072.015    62609.118  \n",
       "185    67120.715    78072.015    62609.118    58492.030  \n",
       "186    78072.015    62609.118    58492.030    48013.183  \n",
       "187    62609.118    58492.030    48013.183    50534.526  \n",
       "188    58492.030    48013.183    50534.526    45594.032  \n",
       "\n",
       "[189 rows x 19 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd_trade_history"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77456551",
   "metadata": {},
   "source": [
    "## 步骤4：数据预处理（在Python中）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c7a5980d-7672-4cd9-8bd9-eab12ae90fa6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_X = pd_trade_history.iloc[:,:-5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2931ec07-4a03-43ad-a2a4-0d720ad6f75b",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>t_p_1_mth_n</th>\n",
       "      <th>t_p_1_qtr_n</th>\n",
       "      <th>val_n</th>\n",
       "      <th>t_m_1_val_n</th>\n",
       "      <th>t_m_2_val_n</th>\n",
       "      <th>t_m_3_val_n</th>\n",
       "      <th>t_m_4_val_n</th>\n",
       "      <th>t_m_5_val_n</th>\n",
       "      <th>t_m_6_val_n</th>\n",
       "      <th>t_m_7_val_n</th>\n",
       "      <th>t_m_8_val_n</th>\n",
       "      <th>t_m_9_val_n</th>\n",
       "      <th>t_m_10_val_n</th>\n",
       "      <th>t_m_11_val_n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>2</td>\n",
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       "      <td>84253.446</td>\n",
       "      <td>83703.337</td>\n",
       "      <td>80723.902</td>\n",
       "      <td>82935.579</td>\n",
       "      <td>78687.679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>46029.894</td>\n",
       "      <td>53569.052</td>\n",
       "      <td>61761.267</td>\n",
       "      <td>59350.408</td>\n",
       "      <td>75445.413</td>\n",
       "      <td>83676.968</td>\n",
       "      <td>100006.244</td>\n",
       "      <td>92713.317</td>\n",
       "      <td>84253.446</td>\n",
       "      <td>83703.337</td>\n",
       "      <td>80723.902</td>\n",
       "      <td>82935.579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>62952.670</td>\n",
       "      <td>46029.894</td>\n",
       "      <td>53569.052</td>\n",
       "      <td>61761.267</td>\n",
       "      <td>59350.408</td>\n",
       "      <td>75445.413</td>\n",
       "      <td>83676.968</td>\n",
       "      <td>100006.244</td>\n",
       "      <td>92713.317</td>\n",
       "      <td>84253.446</td>\n",
       "      <td>83703.337</td>\n",
       "      <td>80723.902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>44073.768</td>\n",
       "      <td>62952.670</td>\n",
       "      <td>46029.894</td>\n",
       "      <td>53569.052</td>\n",
       "      <td>61761.267</td>\n",
       "      <td>59350.408</td>\n",
       "      <td>75445.413</td>\n",
       "      <td>83676.968</td>\n",
       "      <td>100006.244</td>\n",
       "      <td>92713.317</td>\n",
       "      <td>84253.446</td>\n",
       "      <td>83703.337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>44767.966</td>\n",
       "      <td>44073.768</td>\n",
       "      <td>62952.670</td>\n",
       "      <td>46029.894</td>\n",
       "      <td>53569.052</td>\n",
       "      <td>61761.267</td>\n",
       "      <td>59350.408</td>\n",
       "      <td>75445.413</td>\n",
       "      <td>83676.968</td>\n",
       "      <td>100006.244</td>\n",
       "      <td>92713.317</td>\n",
       "      <td>84253.446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>35394.170</td>\n",
       "      <td>27416.917</td>\n",
       "      <td>31506.404</td>\n",
       "      <td>37508.317</td>\n",
       "      <td>51157.470</td>\n",
       "      <td>54108.365</td>\n",
       "      <td>48998.221</td>\n",
       "      <td>63460.576</td>\n",
       "      <td>64331.206</td>\n",
       "      <td>78969.654</td>\n",
       "      <td>71836.554</td>\n",
       "      <td>61451.699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>49345.909</td>\n",
       "      <td>35394.170</td>\n",
       "      <td>27416.917</td>\n",
       "      <td>31506.404</td>\n",
       "      <td>37508.317</td>\n",
       "      <td>51157.470</td>\n",
       "      <td>54108.365</td>\n",
       "      <td>48998.221</td>\n",
       "      <td>63460.576</td>\n",
       "      <td>64331.206</td>\n",
       "      <td>78969.654</td>\n",
       "      <td>71836.554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>67120.715</td>\n",
       "      <td>49345.909</td>\n",
       "      <td>35394.170</td>\n",
       "      <td>27416.917</td>\n",
       "      <td>31506.404</td>\n",
       "      <td>37508.317</td>\n",
       "      <td>51157.470</td>\n",
       "      <td>54108.365</td>\n",
       "      <td>48998.221</td>\n",
       "      <td>63460.576</td>\n",
       "      <td>64331.206</td>\n",
       "      <td>78969.654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>78072.015</td>\n",
       "      <td>67120.715</td>\n",
       "      <td>49345.909</td>\n",
       "      <td>35394.170</td>\n",
       "      <td>27416.917</td>\n",
       "      <td>31506.404</td>\n",
       "      <td>37508.317</td>\n",
       "      <td>51157.470</td>\n",
       "      <td>54108.365</td>\n",
       "      <td>48998.221</td>\n",
       "      <td>63460.576</td>\n",
       "      <td>64331.206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>10</td>\n",
       "      <td>3.0</td>\n",
       "      <td>62609.118</td>\n",
       "      <td>78072.015</td>\n",
       "      <td>67120.715</td>\n",
       "      <td>49345.909</td>\n",
       "      <td>35394.170</td>\n",
       "      <td>27416.917</td>\n",
       "      <td>31506.404</td>\n",
       "      <td>37508.317</td>\n",
       "      <td>51157.470</td>\n",
       "      <td>54108.365</td>\n",
       "      <td>48998.221</td>\n",
       "      <td>63460.576</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>189 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     t_p_1_mth_n  t_p_1_qtr_n      val_n  t_m_1_val_n  t_m_2_val_n  \\\n",
       "0              2          0.0  53569.052    61761.267    59350.408   \n",
       "1              3          1.0  46029.894    53569.052    61761.267   \n",
       "2              4          1.0  62952.670    46029.894    53569.052   \n",
       "3              5          1.0  44073.768    62952.670    46029.894   \n",
       "4              6          2.0  44767.966    44073.768    62952.670   \n",
       "..           ...          ...        ...          ...          ...   \n",
       "184            6          2.0  35394.170    27416.917    31506.404   \n",
       "185            7          2.0  49345.909    35394.170    27416.917   \n",
       "186            8          2.0  67120.715    49345.909    35394.170   \n",
       "187            9          3.0  78072.015    67120.715    49345.909   \n",
       "188           10          3.0  62609.118    78072.015    67120.715   \n",
       "\n",
       "     t_m_3_val_n  t_m_4_val_n  t_m_5_val_n  t_m_6_val_n  t_m_7_val_n  \\\n",
       "0      75445.413    83676.968   100006.244    92713.317    84253.446   \n",
       "1      59350.408    75445.413    83676.968   100006.244    92713.317   \n",
       "2      61761.267    59350.408    75445.413    83676.968   100006.244   \n",
       "3      53569.052    61761.267    59350.408    75445.413    83676.968   \n",
       "4      46029.894    53569.052    61761.267    59350.408    75445.413   \n",
       "..           ...          ...          ...          ...          ...   \n",
       "184    37508.317    51157.470    54108.365    48998.221    63460.576   \n",
       "185    31506.404    37508.317    51157.470    54108.365    48998.221   \n",
       "186    27416.917    31506.404    37508.317    51157.470    54108.365   \n",
       "187    35394.170    27416.917    31506.404    37508.317    51157.470   \n",
       "188    49345.909    35394.170    27416.917    31506.404    37508.317   \n",
       "\n",
       "     t_m_8_val_n  t_m_9_val_n  t_m_10_val_n  t_m_11_val_n  \n",
       "0      83703.337    80723.902     82935.579     78687.679  \n",
       "1      84253.446    83703.337     80723.902     82935.579  \n",
       "2      92713.317    84253.446     83703.337     80723.902  \n",
       "3     100006.244    92713.317     84253.446     83703.337  \n",
       "4      83676.968   100006.244     92713.317     84253.446  \n",
       "..           ...          ...           ...           ...  \n",
       "184    64331.206    78969.654     71836.554     61451.699  \n",
       "185    63460.576    64331.206     78969.654     71836.554  \n",
       "186    48998.221    63460.576     64331.206     78969.654  \n",
       "187    54108.365    48998.221     63460.576     64331.206  \n",
       "188    51157.470    54108.365     48998.221     63460.576  \n",
       "\n",
       "[189 rows x 14 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "63dc3d52-a095-440d-b0e6-88a551374950",
   "metadata": {},
   "outputs": [],
   "source": [
    "np_X = df_X.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "130ba7da-8ae9-478e-82e8-5391a8cfef8e",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.0000000e+00, 0.0000000e+00, 5.3569052e+04, ..., 8.0723902e+04,\n",
       "        8.2935579e+04, 7.8687679e+04],\n",
       "       [3.0000000e+00, 1.0000000e+00, 4.6029894e+04, ..., 8.3703337e+04,\n",
       "        8.0723902e+04, 8.2935579e+04],\n",
       "       [4.0000000e+00, 1.0000000e+00, 6.2952670e+04, ..., 8.4253446e+04,\n",
       "        8.3703337e+04, 8.0723902e+04],\n",
       "       ...,\n",
       "       [8.0000000e+00, 2.0000000e+00, 6.7120715e+04, ..., 6.3460576e+04,\n",
       "        6.4331206e+04, 7.8969654e+04],\n",
       "       [9.0000000e+00, 3.0000000e+00, 7.8072015e+04, ..., 4.8998221e+04,\n",
       "        6.3460576e+04, 6.4331206e+04],\n",
       "       [1.0000000e+01, 3.0000000e+00, 6.2609118e+04, ..., 5.4108365e+04,\n",
       "        4.8998221e+04, 6.3460576e+04]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "57386ba8-5c9a-473c-84a2-dc36c551cee7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_Y = pd_trade_history.iloc[:,-4:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "24c681a3-a090-4fb3-86a0-52fdc173e07b",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>t_p_1_val_n</th>\n",
       "      <th>t_p_2_val_n</th>\n",
       "      <th>t_p_3_val_n</th>\n",
       "      <th>t_p_4_val_n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>46029.894</td>\n",
       "      <td>62952.670</td>\n",
       "      <td>44073.768</td>\n",
       "      <td>44767.966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>62952.670</td>\n",
       "      <td>44073.768</td>\n",
       "      <td>44767.966</td>\n",
       "      <td>64112.377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44073.768</td>\n",
       "      <td>44767.966</td>\n",
       "      <td>64112.377</td>\n",
       "      <td>88478.110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44767.966</td>\n",
       "      <td>64112.377</td>\n",
       "      <td>88478.110</td>\n",
       "      <td>80440.587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>64112.377</td>\n",
       "      <td>88478.110</td>\n",
       "      <td>80440.587</td>\n",
       "      <td>75297.618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>49345.909</td>\n",
       "      <td>67120.715</td>\n",
       "      <td>78072.015</td>\n",
       "      <td>62609.118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>67120.715</td>\n",
       "      <td>78072.015</td>\n",
       "      <td>62609.118</td>\n",
       "      <td>58492.030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>78072.015</td>\n",
       "      <td>62609.118</td>\n",
       "      <td>58492.030</td>\n",
       "      <td>48013.183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>62609.118</td>\n",
       "      <td>58492.030</td>\n",
       "      <td>48013.183</td>\n",
       "      <td>50534.526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>58492.030</td>\n",
       "      <td>48013.183</td>\n",
       "      <td>50534.526</td>\n",
       "      <td>45594.032</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>189 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     t_p_1_val_n  t_p_2_val_n  t_p_3_val_n  t_p_4_val_n\n",
       "0      46029.894    62952.670    44073.768    44767.966\n",
       "1      62952.670    44073.768    44767.966    64112.377\n",
       "2      44073.768    44767.966    64112.377    88478.110\n",
       "3      44767.966    64112.377    88478.110    80440.587\n",
       "4      64112.377    88478.110    80440.587    75297.618\n",
       "..           ...          ...          ...          ...\n",
       "184    49345.909    67120.715    78072.015    62609.118\n",
       "185    67120.715    78072.015    62609.118    58492.030\n",
       "186    78072.015    62609.118    58492.030    48013.183\n",
       "187    62609.118    58492.030    48013.183    50534.526\n",
       "188    58492.030    48013.183    50534.526    45594.032\n",
       "\n",
       "[189 rows x 4 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2c240977-232a-4269-8a43-779036b89c91",
   "metadata": {},
   "outputs": [],
   "source": [
    "np_Y = df_Y.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7e893cb9-64c2-48bb-b3a0-1b59ac9ceffa",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 46029.894  ,  62952.67   ,  44073.768  ,  44767.966  ],\n",
       "       [ 62952.67   ,  44073.768  ,  44767.966  ,  64112.377  ],\n",
       "       [ 44073.768  ,  44767.966  ,  64112.377  ,  88478.11   ],\n",
       "       [ 44767.966  ,  64112.377  ,  88478.11   ,  80440.587  ],\n",
       "       [ 64112.377  ,  88478.11   ,  80440.587  ,  75297.618  ],\n",
       "       [ 88478.11   ,  80440.587  ,  75297.618  ,  57203.896  ],\n",
       "       [ 80440.587  ,  75297.618  ,  57203.896  ,  52005.891  ],\n",
       "       [ 75297.618  ,  57203.896  ,  52005.891  ,  57695.276  ],\n",
       "       [ 57203.896  ,  52005.891  ,  57695.276  ,  52962.69   ],\n",
       "       [ 52005.891  ,  57695.276  ,  52962.69   ,  51480.011  ],\n",
       "       [ 57695.276  ,  52962.69   ,  51480.011  ,  52363.935  ],\n",
       "       [ 52962.69   ,  51480.011  ,  52363.935  ,  43189.544  ],\n",
       "       [ 51480.011  ,  52363.935  ,  43189.544  ,  40836.79   ],\n",
       "       [ 52363.935  ,  43189.544  ,  40836.79   ,  48698.108  ],\n",
       "       [ 43189.544  ,  40836.79   ,  48698.108  ,  87492.169  ],\n",
       "       [ 40836.79   ,  48698.108  ,  87492.169  ,  81851.383  ],\n",
       "       [ 48698.108  ,  87492.169  ,  81851.383  ,  74231.752  ],\n",
       "       [ 87492.169  ,  81851.383  ,  74231.752  ,  68665.841  ],\n",
       "       [ 81851.383  ,  74231.752  ,  68665.841  ,  51326.54   ],\n",
       "       [ 74231.752  ,  68665.841  ,  51326.54   ,  52244.471  ],\n",
       "       [ 68665.841  ,  51326.54   ,  52244.471  ,  52770.41814],\n",
       "       [ 51326.54   ,  52244.471  ,  52770.41814,  53788.79565],\n",
       "       [ 52244.471  ,  52770.41814,  53788.79565,  60294.52118],\n",
       "       [ 52770.41814,  53788.79565,  60294.52118,  56992.28362],\n",
       "       [ 53788.79565,  60294.52118,  56992.28362,  56397.63791],\n",
       "       [ 60294.52118,  56992.28362,  56397.63791,  56402.3506 ],\n",
       "       [ 56992.28362,  56397.63791,  56402.3506 ,  84330.17657],\n",
       "       [ 56397.63791,  56402.3506 ,  84330.17657,  81551.34279],\n",
       "       [ 56402.3506 ,  84330.17657,  81551.34279,  77111.85826],\n",
       "       [ 84330.17657,  81551.34279,  77111.85826,  65163.11274],\n",
       "       [ 81551.34279,  77111.85826,  65163.11274,  63337.60542],\n",
       "       [ 77111.85826,  65163.11274,  63337.60542,  62377.07202],\n",
       "       [ 65163.11274,  63337.60542,  62377.07202,  52728.94133],\n",
       "       [ 63337.60542,  62377.07202,  52728.94133,  45266.09349],\n",
       "       [ 62377.07202,  52728.94133,  45266.09349,  47896.94495],\n",
       "       [ 52728.94133,  45266.09349,  47896.94495,  44238.58208],\n",
       "       [ 45266.09349,  47896.94495,  44238.58208,  46817.56708],\n",
       "       [ 47896.94495,  44238.58208,  46817.56708,  48796.76647],\n",
       "       [ 44238.58208,  46817.56708,  48796.76647,  86223.71598],\n",
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       "       [ 48796.76647,  86223.71598,  86927.03486,  61078.61581],\n",
       "       [ 86223.71598,  86927.03486,  61078.61581,  54455.08686],\n",
       "       [ 86927.03486,  61078.61581,  54455.08686,  55973.38929],\n",
       "       [ 61078.61581,  54455.08686,  55973.38929,  58766.55159],\n",
       "       [ 54455.08686,  55973.38929,  58766.55159,  49457.09781],\n",
       "       [ 55973.38929,  58766.55159,  49457.09781,  46922.44812],\n",
       "       [ 58766.55159,  49457.09781,  46922.44812,  53098.96997],\n",
       "       [ 49457.09781,  46922.44812,  53098.96997,  44123.38798],\n",
       "       [ 46922.44812,  53098.96997,  44123.38798,  54018.92224],\n",
       "       [ 53098.96997,  44123.38798,  54018.92224,  66829.1947 ],\n",
       "       [ 44123.38798,  54018.92224,  66829.1947 , 107131.36377],\n",
       "       [ 54018.92224,  66829.1947 , 107131.36377,  82272.81129],\n",
       "       [ 66829.1947 , 107131.36377,  82272.81129,  74886.21128],\n",
       "       [107131.36377,  82272.81129,  74886.21128,  66357.20312],\n",
       "       [ 82272.81129,  74886.21128,  66357.20312,  60771.08901],\n",
       "       [ 74886.21128,  66357.20312,  60771.08901,  64967.41426],\n",
       "       [ 66357.20312,  60771.08901,  64967.41426,  58108.169  ],\n",
       "       [ 60771.08901,  64967.41426,  58108.169  ,  52340.724  ],\n",
       "       [ 64967.41426,  58108.169  ,  52340.724  ,  49607.869  ],\n",
       "       [ 58108.169  ,  52340.724  ,  49607.869  ,  50513.038  ],\n",
       "       [ 52340.724  ,  49607.869  ,  50513.038  ,  55662.612  ],\n",
       "       [ 49607.869  ,  50513.038  ,  55662.612  ,  68901.792  ],\n",
       "       [ 50513.038  ,  55662.612  ,  68901.792  ,  94285.701  ],\n",
       "       [ 55662.612  ,  68901.792  ,  94285.701  , 105147.547  ],\n",
       "       [ 68901.792  ,  94285.701  , 105147.547  ,  78587.791  ],\n",
       "       [ 94285.701  , 105147.547  ,  78587.791  ,  75091.391  ],\n",
       "       [105147.547  ,  78587.791  ,  75091.391  ,  69765.838  ],\n",
       "       [ 78587.791  ,  75091.391  ,  69765.838  ,  76273.906  ],\n",
       "       [ 75091.391  ,  69765.838  ,  76273.906  ,  75456.112  ],\n",
       "       [ 69765.838  ,  76273.906  ,  75456.112  ,  61883.929  ],\n",
       "       [ 76273.906  ,  75456.112  ,  61883.929  ,  58875.843  ],\n",
       "       [ 75456.112  ,  61883.929  ,  58875.843  ,  68652.542  ],\n",
       "       [ 61883.929  ,  58875.843  ,  68652.542  ,  54823.788  ],\n",
       "       [ 58875.843  ,  68652.542  ,  54823.788  ,  61084.759  ],\n",
       "       [ 68652.542  ,  54823.788  ,  61084.759  ,  79246.866  ],\n",
       "       [ 54823.788  ,  61084.759  ,  79246.866  ,  93648.667  ],\n",
       "       [ 61084.759  ,  79246.866  ,  93648.667  ,  83544.695  ],\n",
       "       [ 79246.866  ,  93648.667  ,  83544.695  ,  77850.204  ],\n",
       "       [ 93648.667  ,  83544.695  ,  77850.204  ,  68313.812  ],\n",
       "       [ 83544.695  ,  77850.204  ,  68313.812  ,  74486.236  ],\n",
       "       [ 77850.204  ,  68313.812  ,  74486.236  ,  66134.977  ],\n",
       "       [ 68313.812  ,  74486.236  ,  66134.977  ,  59822.344  ],\n",
       "       [ 74486.236  ,  66134.977  ,  59822.344  ,  55354.512  ],\n",
       "       [ 66134.977  ,  59822.344  ,  55354.512  ,  49661.081  ],\n",
       "       [ 59822.344  ,  55354.512  ,  49661.081  ,  51598.042  ],\n",
       "       [ 55354.512  ,  49661.081  ,  51598.042  ,  46106.979  ],\n",
       "       [ 49661.081  ,  51598.042  ,  46106.979  ,  87393.479  ],\n",
       "       [ 51598.042  ,  46106.979  ,  87393.479  ,  89115.446  ],\n",
       "       [ 46106.979  ,  87393.479  ,  89115.446  ,  96365.543  ],\n",
       "       [ 87393.479  ,  89115.446  ,  96365.543  ,  71254.818  ],\n",
       "       [ 89115.446  ,  96365.543  ,  71254.818  ,  63515.255  ],\n",
       "       [ 96365.543  ,  71254.818  ,  63515.255  ,  72605.64   ],\n",
       "       [ 71254.818  ,  63515.255  ,  72605.64   ,  66583.144  ],\n",
       "       [ 63515.255  ,  72605.64   ,  66583.144  ,  60736.825  ],\n",
       "       [ 72605.64   ,  66583.144  ,  60736.825  ,  65508.919  ],\n",
       "       [ 66583.144  ,  60736.825  ,  65508.919  ,  54720.878  ],\n",
       "       [ 60736.825  ,  65508.919  ,  54720.878  ,  39235.991  ],\n",
       "       [ 65508.919  ,  54720.878  ,  39235.991  ,  37084.709  ],\n",
       "       [ 54720.878  ,  39235.991  ,  37084.709  ,  64050.878  ],\n",
       "       [ 39235.991  ,  37084.709  ,  64050.878  ,  76274.58   ],\n",
       "       [ 37084.709  ,  64050.878  ,  76274.58   ,  69752.197  ],\n",
       "       [ 64050.878  ,  76274.58   ,  69752.197  ,  76902.356  ],\n",
       "       [ 76274.58   ,  69752.197  ,  76902.356  ,  64943.749  ],\n",
       "       [ 69752.197  ,  76902.356  ,  64943.749  ,  60297.428  ],\n",
       "       [ 76902.356  ,  64943.749  ,  60297.428  ,  51610.321  ],\n",
       "       [ 64943.749  ,  60297.428  ,  51610.321  ,  51394.206  ],\n",
       "       [ 60297.428  ,  51610.321  ,  51394.206  ,  41357.242  ],\n",
       "       [ 51610.321  ,  51394.206  ,  41357.242  ,  35375.384  ],\n",
       "       [ 51394.206  ,  41357.242  ,  35375.384  ,  30546.195  ],\n",
       "       [ 41357.242  ,  35375.384  ,  30546.195  ,  34435.901  ],\n",
       "       [ 35375.384  ,  30546.195  ,  34435.901  ,  56667.648  ],\n",
       "       [ 30546.195  ,  34435.901  ,  56667.648  ,  69261.433  ],\n",
       "       [ 34435.901  ,  56667.648  ,  69261.433  ,  69651.242  ],\n",
       "       [ 56667.648  ,  69261.433  ,  69651.242  ,  54480.865  ],\n",
       "       [ 69261.433  ,  69651.242  ,  54480.865  ,  53507.706  ],\n",
       "       [ 69651.242  ,  54480.865  ,  53507.706  ,  68275.836  ],\n",
       "       [ 54480.865  ,  53507.706  ,  68275.836  ,  65556.392  ],\n",
       "       [ 53507.706  ,  68275.836  ,  65556.392  ,  65298.761  ],\n",
       "       [ 68275.836  ,  65556.392  ,  65298.761  ,  63258.409  ],\n",
       "       [ 65556.392  ,  65298.761  ,  63258.409  ,  59439.288  ],\n",
       "       [ 65298.761  ,  63258.409  ,  59439.288  ,  59554.117  ],\n",
       "       [ 63258.409  ,  59439.288  ,  59554.117  ,  62632.389  ],\n",
       "       [ 59439.288  ,  59554.117  ,  62632.389  ,  73811.646  ],\n",
       "       [ 59554.117  ,  62632.389  ,  73811.646  , 101387.936  ],\n",
       "       [ 62632.389  ,  73811.646  , 101387.936  ,  89558.213  ],\n",
       "       [ 73811.646  , 101387.936  ,  89558.213  ,  82059.796  ],\n",
       "       [101387.936  ,  89558.213  ,  82059.796  ,  68921.573  ],\n",
       "       [ 89558.213  ,  82059.796  ,  68921.573  ,  63863.155  ],\n",
       "       [ 82059.796  ,  68921.573  ,  63863.155  ,  65611.019  ],\n",
       "       [ 68921.573  ,  63863.155  ,  65611.019  ,  60459.618  ],\n",
       "       [ 63863.155  ,  65611.019  ,  60459.618  ,  58409.919  ],\n",
       "       [ 65611.019  ,  60459.618  ,  58409.919  ,  50993.018  ],\n",
       "       [ 60459.618  ,  58409.919  ,  50993.018  ,  54819.653  ],\n",
       "       [ 58409.919  ,  50993.018  ,  54819.653  ,  64712.59   ],\n",
       "       [ 50993.018  ,  54819.653  ,  64712.59   ,  82591.903  ],\n",
       "       [ 54819.653  ,  64712.59   ,  82591.903  ,  83178.844  ],\n",
       "       [ 64712.59   ,  82591.903  ,  83178.844  ,  83419.959  ],\n",
       "       [ 82591.903  ,  83178.844  ,  83419.959  ,  69868.955  ],\n",
       "       [ 83178.844  ,  83419.959  ,  69868.955  ,  69803.291  ],\n",
       "       [ 83419.959  ,  69868.955  ,  69803.291  ,  81843.838  ],\n",
       "       [ 69868.955  ,  69803.291  ,  81843.838  ,  72448.147  ],\n",
       "       [ 69803.291  ,  81843.838  ,  72448.147  ,  68285.09   ],\n",
       "       [ 81843.838  ,  72448.147  ,  68285.09   ,  58980.168  ],\n",
       "       [ 72448.147  ,  68285.09   ,  58980.168  ,  56546.022  ],\n",
       "       [ 68285.09   ,  58980.168  ,  56546.022  ,  57045.682  ],\n",
       "       [ 58980.168  ,  56546.022  ,  57045.682  ,  56205.437  ],\n",
       "       [ 56546.022  ,  57045.682  ,  56205.437  ,  80285.053  ],\n",
       "       [ 57045.682  ,  56205.437  ,  80285.053  ,  82009.471  ],\n",
       "       [ 56205.437  ,  80285.053  ,  82009.471  ,  86626.264  ],\n",
       "       [ 80285.053  ,  82009.471  ,  86626.264  ,  85776.381  ],\n",
       "       [ 82009.471  ,  86626.264  ,  85776.381  ,  61219.037  ],\n",
       "       [ 86626.264  ,  85776.381  ,  61219.037  ,  66095.36   ],\n",
       "       [ 85776.381  ,  61219.037  ,  66095.36   ,  62815.61   ],\n",
       "       [ 61219.037  ,  66095.36   ,  62815.61   ,  46537.062  ],\n",
       "       [ 66095.36   ,  62815.61   ,  46537.062  ,  54168.439  ],\n",
       "       [ 62815.61   ,  46537.062  ,  54168.439  ,  52670.906  ],\n",
       "       [ 46537.062  ,  54168.439  ,  52670.906  ,  50950.93   ],\n",
       "       [ 54168.439  ,  52670.906  ,  50950.93   ,  73308.817  ],\n",
       "       [ 52670.906  ,  50950.93   ,  73308.817  ,  83645.461  ],\n",
       "       [ 50950.93   ,  73308.817  ,  83645.461  ,  88849.753  ],\n",
       "       [ 73308.817  ,  83645.461  ,  88849.753  ,  87498.43   ],\n",
       "       [ 83645.461  ,  88849.753  ,  87498.43   ,  83347.307  ],\n",
       "       [ 88849.753  ,  87498.43   ,  83347.307  ,  59143.706  ],\n",
       "       [ 87498.43   ,  83347.307  ,  59143.706  ,  62996.464  ],\n",
       "       [ 83347.307  ,  59143.706  ,  62996.464  ,  60932.085  ],\n",
       "       [ 59143.706  ,  62996.464  ,  60932.085  ,  48598.881  ],\n",
       "       [ 62996.464  ,  60932.085  ,  48598.881  ,  38153.736  ],\n",
       "       [ 60932.085  ,  48598.881  ,  38153.736  ,  36114.524  ],\n",
       "       [ 48598.881  ,  38153.736  ,  36114.524  ,  39061.798  ],\n",
       "       [ 38153.736  ,  36114.524  ,  39061.798  ,  61451.699  ],\n",
       "       [ 36114.524  ,  39061.798  ,  61451.699  ,  71836.554  ],\n",
       "       [ 39061.798  ,  61451.699  ,  71836.554  ,  78969.654  ],\n",
       "       [ 61451.699  ,  71836.554  ,  78969.654  ,  64331.206  ],\n",
       "       [ 71836.554  ,  78969.654  ,  64331.206  ,  63460.576  ],\n",
       "       [ 78969.654  ,  64331.206  ,  63460.576  ,  48998.221  ],\n",
       "       [ 64331.206  ,  63460.576  ,  48998.221  ,  54108.365  ],\n",
       "       [ 63460.576  ,  48998.221  ,  54108.365  ,  51157.47   ],\n",
       "       [ 48998.221  ,  54108.365  ,  51157.47   ,  37508.317  ],\n",
       "       [ 54108.365  ,  51157.47   ,  37508.317  ,  31506.404  ],\n",
       "       [ 51157.47   ,  37508.317  ,  31506.404  ,  27416.917  ],\n",
       "       [ 37508.317  ,  31506.404  ,  27416.917  ,  35394.17   ],\n",
       "       [ 31506.404  ,  27416.917  ,  35394.17   ,  49345.909  ],\n",
       "       [ 27416.917  ,  35394.17   ,  49345.909  ,  67120.715  ],\n",
       "       [ 35394.17   ,  49345.909  ,  67120.715  ,  78072.015  ],\n",
       "       [ 49345.909  ,  67120.715  ,  78072.015  ,  62609.118  ],\n",
       "       [ 67120.715  ,  78072.015  ,  62609.118  ,  58492.03   ],\n",
       "       [ 78072.015  ,  62609.118  ,  58492.03   ,  48013.183  ],\n",
       "       [ 62609.118  ,  58492.03   ,  48013.183  ,  50534.526  ],\n",
       "       [ 58492.03   ,  48013.183  ,  50534.526  ,  45594.032  ]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_Y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c794878-e666-4c04-a44b-c99843843dcb",
   "metadata": {},
   "source": [
    "### 4.b : 准备训练集和测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7dcb034d-24b2-45bc-92d8-f0d270f01c6f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_训练集, X_测试集, Y_训练集, Y_测试集 = train_test_split(np_X, np_Y, test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f495c617-4eb2-4383-96f9-e857ccce81d7",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[8.00000000e+00, 2.00000000e+00, 8.84781100e+04, ...,\n",
       "        7.54454130e+04, 8.36769680e+04, 1.00006244e+05],\n",
       "       [6.00000000e+00, 2.00000000e+00, 3.05461950e+04, ...,\n",
       "        7.62745800e+04, 6.40508780e+04, 3.70847090e+04],\n",
       "       [1.20000000e+01, 4.00000000e+00, 5.13265400e+04, ...,\n",
       "        5.14800110e+04, 5.29626900e+04, 5.76952760e+04],\n",
       "       ...,\n",
       "       [1.00000000e+01, 3.00000000e+00, 7.71118583e+04, ...,\n",
       "        5.22444710e+04, 5.13265400e+04, 6.86658410e+04],\n",
       "       [2.00000000e+00, 0.00000000e+00, 5.11574700e+04, ...,\n",
       "        3.61145240e+04, 3.81537360e+04, 4.85988810e+04],\n",
       "       [9.00000000e+00, 3.00000000e+00, 7.89696540e+04, ...,\n",
       "        5.91437060e+04, 8.33473070e+04, 8.74984300e+04]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_训练集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "67a1f97e-aac4-403b-9d3f-4a49bc8f1a79",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.00000000e+00, 0.00000000e+00, 7.62739060e+04, 6.97658380e+04,\n",
       "        7.50913910e+04, 7.85877910e+04, 1.05147547e+05, 9.42857010e+04,\n",
       "        6.89017920e+04, 5.56626120e+04, 5.05130380e+04, 4.96078690e+04,\n",
       "        5.23407240e+04, 5.81081690e+04],\n",
       "       [8.00000000e+00, 2.00000000e+00, 7.38116460e+04, 6.26323890e+04,\n",
       "        5.95541170e+04, 5.94392880e+04, 6.32584090e+04, 6.52987610e+04,\n",
       "        6.55563920e+04, 6.82758360e+04, 5.35077060e+04, 5.44808650e+04,\n",
       "        6.96512420e+04, 6.92614330e+04],\n",
       "       [4.00000000e+00, 1.00000000e+00, 6.29526700e+04, 4.60298940e+04,\n",
       "        5.35690520e+04, 6.17612670e+04, 5.93504080e+04, 7.54454130e+04,\n",
       "        8.36769680e+04, 1.00006244e+05, 9.27133170e+04, 8.42534460e+04,\n",
       "        8.37033370e+04, 8.07239020e+04],\n",
       "       [7.00000000e+00, 2.00000000e+00, 6.14516990e+04, 3.90617980e+04,\n",
       "        3.61145240e+04, 3.81537360e+04, 4.85988810e+04, 6.09320850e+04,\n",
       "        6.29964640e+04, 5.91437060e+04, 8.33473070e+04, 8.74984300e+04,\n",
       "        8.88497530e+04, 8.36454610e+04],\n",
       "       [4.00000000e+00, 1.00000000e+00, 5.53545120e+04, 5.98223440e+04,\n",
       "        6.61349770e+04, 7.44862360e+04, 6.83138120e+04, 7.78502040e+04,\n",
       "        8.35446950e+04, 9.36486670e+04, 7.92468660e+04, 6.10847590e+04,\n",
       "        5.48237880e+04, 6.86525420e+04],\n",
       "       [1.20000000e+01, 4.00000000e+00, 6.83138120e+04, 7.78502040e+04,\n",
       "        8.35446950e+04, 9.36486670e+04, 7.92468660e+04, 6.10847590e+04,\n",
       "        5.48237880e+04, 6.86525420e+04, 5.88758430e+04, 6.18839290e+04,\n",
       "        7.54561120e+04, 7.62739060e+04],\n",
       "       [3.00000000e+00, 1.00000000e+00, 4.52660935e+04, 5.27289413e+04,\n",
       "        6.23770720e+04, 6.33376054e+04, 6.51631127e+04, 7.71118583e+04,\n",
       "        8.15513428e+04, 8.43301766e+04, 5.64023506e+04, 5.63976379e+04,\n",
       "        5.69922836e+04, 6.02945212e+04],\n",
       "       [1.00000000e+00, 0.00000000e+00, 5.22444710e+04, 5.13265400e+04,\n",
       "        6.86658410e+04, 7.42317520e+04, 8.18513830e+04, 8.74921690e+04,\n",
       "        4.86981080e+04, 4.08367900e+04, 4.31895440e+04, 5.23639350e+04,\n",
       "        5.14800110e+04, 5.29626900e+04],\n",
       "       [2.00000000e+00, 0.00000000e+00, 5.81081690e+04, 6.49674143e+04,\n",
       "        6.07710890e+04, 6.63572031e+04, 7.48862113e+04, 8.22728113e+04,\n",
       "        1.07131364e+05, 6.68291947e+04, 5.40189222e+04, 4.41233880e+04,\n",
       "        5.30989700e+04, 4.69224481e+04],\n",
       "       [8.00000000e+00, 2.00000000e+00, 8.74921690e+04, 4.86981080e+04,\n",
       "        4.08367900e+04, 4.31895440e+04, 5.23639350e+04, 5.14800110e+04,\n",
       "        5.29626900e+04, 5.76952760e+04, 5.20058910e+04, 5.72038960e+04,\n",
       "        7.52976180e+04, 8.04405870e+04],\n",
       "       [3.00000000e+00, 1.00000000e+00, 6.82850900e+04, 7.24481470e+04,\n",
       "        8.18438380e+04, 6.98032910e+04, 6.98689550e+04, 8.34199590e+04,\n",
       "        8.31788440e+04, 8.25919030e+04, 6.47125900e+04, 5.48196530e+04,\n",
       "        5.09930180e+04, 5.84099190e+04],\n",
       "       [1.10000000e+01, 3.00000000e+00, 6.34605760e+04, 6.43312060e+04,\n",
       "        7.89696540e+04, 7.18365540e+04, 6.14516990e+04, 3.90617980e+04,\n",
       "        3.61145240e+04, 3.81537360e+04, 4.85988810e+04, 6.09320850e+04,\n",
       "        6.29964640e+04, 5.91437060e+04],\n",
       "       [1.10000000e+01, 3.00000000e+00, 6.63572031e+04, 7.48862113e+04,\n",
       "        8.22728113e+04, 1.07131364e+05, 6.68291947e+04, 5.40189222e+04,\n",
       "        4.41233880e+04, 5.30989700e+04, 4.69224481e+04, 4.94570978e+04,\n",
       "        5.87665516e+04, 5.59733893e+04],\n",
       "       [4.00000000e+00, 1.00000000e+00, 4.96078690e+04, 5.23407240e+04,\n",
       "        5.81081690e+04, 6.49674143e+04, 6.07710890e+04, 6.63572031e+04,\n",
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       "        8.25919030e+04, 6.47125900e+04],\n",
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       "        3.90617980e+04, 3.61145240e+04, 3.81537360e+04, 4.85988810e+04,\n",
       "        6.09320850e+04, 6.29964640e+04, 5.91437060e+04, 8.33473070e+04,\n",
       "        8.74984300e+04, 8.88497530e+04],\n",
       "       [4.00000000e+00, 1.00000000e+00, 5.23639350e+04, 5.14800110e+04,\n",
       "        5.29626900e+04, 5.76952760e+04, 5.20058910e+04, 5.72038960e+04,\n",
       "        7.52976180e+04, 8.04405870e+04, 8.84781100e+04, 6.41123770e+04,\n",
       "        4.47679660e+04, 4.40737680e+04],\n",
       "       [9.00000000e+00, 3.00000000e+00, 8.22728113e+04, 1.07131364e+05,\n",
       "        6.68291947e+04, 5.40189222e+04, 4.41233880e+04, 5.30989700e+04,\n",
       "        4.69224481e+04, 4.94570978e+04, 5.87665516e+04, 5.59733893e+04,\n",
       "        5.44550869e+04, 6.10786158e+04],\n",
       "       [1.10000000e+01, 3.00000000e+00, 7.50913910e+04, 7.85877910e+04,\n",
       "        1.05147547e+05, 9.42857010e+04, 6.89017920e+04, 5.56626120e+04,\n",
       "        5.05130380e+04, 4.96078690e+04, 5.23407240e+04, 5.81081690e+04,\n",
       "        6.49674143e+04, 6.07710890e+04]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e9d0b2fd-b109-42b0-b2d9-b6dbf2e86547",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "       [ 61084.759  ,  79246.866  ,  93648.667  ,  83544.695  ],\n",
       "       [ 56992.28362,  56397.63791,  56402.3506 ,  84330.17657],\n",
       "       [ 68275.836  ,  65556.392  ,  65298.761  ,  63258.409  ],\n",
       "       [ 88478.11   ,  80440.587  ,  75297.618  ,  57203.896  ],\n",
       "       [ 89558.213  ,  82059.796  ,  68921.573  ,  63863.155  ],\n",
       "       [ 68652.542  ,  54823.788  ,  61084.759  ,  79246.866  ],\n",
       "       [ 64050.878  ,  76274.58   ,  69752.197  ,  76902.356  ],\n",
       "       [ 77111.85826,  65163.11274,  63337.60542,  62377.07202],\n",
       "       [ 46537.062  ,  54168.439  ,  52670.906  ,  50950.93   ],\n",
       "       [ 54018.92224,  66829.1947 , 107131.36377,  82272.81129],\n",
       "       [ 83178.844  ,  83419.959  ,  69868.955  ,  69803.291  ],\n",
       "       [ 69261.433  ,  69651.242  ,  54480.865  ,  53507.706  ],\n",
       "       [ 58409.919  ,  50993.018  ,  54819.653  ,  64712.59   ],\n",
       "       [ 58492.03   ,  48013.183  ,  50534.526  ,  45594.032  ],\n",
       "       [ 59439.288  ,  59554.117  ,  62632.389  ,  73811.646  ],\n",
       "       [ 87393.479  ,  89115.446  ,  96365.543  ,  71254.818  ],\n",
       "       [ 60297.428  ,  51610.321  ,  51394.206  ,  41357.242  ],\n",
       "       [ 66095.36   ,  62815.61   ,  46537.062  ,  54168.439  ],\n",
       "       [ 39061.798  ,  61451.699  ,  71836.554  ,  78969.654  ],\n",
       "       [ 49457.09781,  46922.44812,  53098.96997,  44123.38798],\n",
       "       [ 79246.866  ,  93648.667  ,  83544.695  ,  77850.204  ],\n",
       "       [ 51157.47   ,  37508.317  ,  31506.404  ,  27416.917  ],\n",
       "       [ 53098.96997,  44123.38798,  54018.92224,  66829.1947 ],\n",
       "       [ 68313.812  ,  74486.236  ,  66134.977  ,  59822.344  ],\n",
       "       [ 56546.022  ,  57045.682  ,  56205.437  ,  80285.053  ],\n",
       "       [ 74231.752  ,  68665.841  ,  51326.54   ,  52244.471  ],\n",
       "       [ 58766.55159,  49457.09781,  46922.44812,  53098.96997],\n",
       "       [ 46029.894  ,  62952.67   ,  44073.768  ,  44767.966  ],\n",
       "       [ 68285.09   ,  58980.168  ,  56546.022  ,  57045.682  ],\n",
       "       [ 86223.71598,  86927.03486,  61078.61581,  54455.08686],\n",
       "       [ 65298.761  ,  63258.409  ,  59439.288  ,  59554.117  ],\n",
       "       [ 60294.52118,  56992.28362,  56397.63791,  56402.3506 ],\n",
       "       [105147.547  ,  78587.791  ,  75091.391  ,  69765.838  ],\n",
       "       [ 49607.869  ,  50513.038  ,  55662.612  ,  68901.792  ],\n",
       "       [ 51598.042  ,  46106.979  ,  87393.479  ,  89115.446  ],\n",
       "       [ 65163.11274,  63337.60542,  62377.07202,  52728.94133],\n",
       "       [ 37508.317  ,  31506.404  ,  27416.917  ,  35394.17   ],\n",
       "       [ 64331.206  ,  63460.576  ,  48998.221  ,  54108.365  ]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_训练集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "11fb78ca-242e-4a2a-8e79-eb384df89231",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 75456.112  ,  61883.929  ,  58875.843  ,  68652.542  ],\n",
       "       [101387.936  ,  89558.213  ,  82059.796  ,  68921.573  ],\n",
       "       [ 44073.768  ,  44767.966  ,  64112.377  ,  88478.11   ],\n",
       "       [ 71836.554  ,  78969.654  ,  64331.206  ,  63460.576  ],\n",
       "       [ 49661.081  ,  51598.042  ,  46106.979  ,  87393.479  ],\n",
       "       [ 74486.236  ,  66134.977  ,  59822.344  ,  55354.512  ],\n",
       "       [ 47896.94495,  44238.58208,  46817.56708,  48796.76647],\n",
       "       [ 52770.41814,  53788.79565,  60294.52118,  56992.28362],\n",
       "       [ 52340.724  ,  49607.869  ,  50513.038  ,  55662.612  ],\n",
       "       [ 81851.383  ,  74231.752  ,  68665.841  ,  51326.54   ],\n",
       "       [ 58980.168  ,  56546.022  ,  57045.682  ,  56205.437  ],\n",
       "       [ 48998.221  ,  54108.365  ,  51157.47   ,  37508.317  ],\n",
       "       [ 60771.08901,  64967.41426,  58108.169  ,  52340.724  ],\n",
       "       [ 50513.038  ,  55662.612  ,  68901.792  ,  94285.701  ],\n",
       "       [ 48698.108  ,  87492.169  ,  81851.383  ,  74231.752  ],\n",
       "       [ 59822.344  ,  55354.512  ,  49661.081  ,  51598.042  ],\n",
       "       [ 66583.144  ,  60736.825  ,  65508.919  ,  54720.878  ],\n",
       "       [ 82272.81129,  74886.21128,  66357.20312,  60771.08901],\n",
       "       [ 45266.09349,  47896.94495,  44238.58208,  46817.56708],\n",
       "       [ 80285.053  ,  82009.471  ,  86626.264  ,  85776.381  ],\n",
       "       [ 88849.753  ,  87498.43   ,  83347.307  ,  59143.706  ],\n",
       "       [ 87498.43   ,  83347.307  ,  59143.706  ,  62996.464  ],\n",
       "       [ 49345.909  ,  67120.715  ,  78072.015  ,  62609.118  ],\n",
       "       [ 82591.903  ,  83178.844  ,  83419.959  ,  69868.955  ],\n",
       "       [ 84330.17657,  81551.34279,  77111.85826,  65163.11274],\n",
       "       [ 63515.255  ,  72605.64   ,  66583.144  ,  60736.825  ],\n",
       "       [ 27416.917  ,  35394.17   ,  49345.909  ,  67120.715  ],\n",
       "       [ 69752.197  ,  76902.356  ,  64943.749  ,  60297.428  ],\n",
       "       [ 63460.576  ,  48998.221  ,  54108.365  ,  51157.47   ],\n",
       "       [ 59143.706  ,  62996.464  ,  60932.085  ,  48598.881  ],\n",
       "       [ 54455.08686,  55973.38929,  58766.55159,  49457.09781],\n",
       "       [ 76273.906  ,  75456.112  ,  61883.929  ,  58875.843  ],\n",
       "       [ 37084.709  ,  64050.878  ,  76274.58   ,  69752.197  ],\n",
       "       [ 56205.437  ,  80285.053  ,  82009.471  ,  86626.264  ],\n",
       "       [ 78969.654  ,  64331.206  ,  63460.576  ,  48998.221  ],\n",
       "       [ 43189.544  ,  40836.79   ,  48698.108  ,  87492.169  ],\n",
       "       [ 74886.21128,  66357.20312,  60771.08901,  64967.41426],\n",
       "       [ 69765.838  ,  76273.906  ,  75456.112  ,  61883.929  ]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_测试集"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cf70d4a-eeca-45cc-b262-4aa63a9d9e85",
   "metadata": {},
   "source": [
    "### 4.c : 将不同量级的输入特征归一化为相同量级"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "416a7f85-f574-437d-b1f3-331d7efce5fc",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4.57009776e-01,  1.51972052e-01,  1.68927549e+00, ...,\n",
       "         7.03286655e-01,  1.29109611e+00,  2.31964956e+00],\n",
       "       [-1.25340234e-01,  1.51972052e-01, -2.13078981e+00, ...,\n",
       "         7.56161432e-01,  5.66783985e-04, -1.78995636e+00],\n",
       "       [ 1.62170980e+00,  1.79109919e+00, -7.60521372e-01, ...,\n",
       "        -8.24952266e-01, -7.28545932e-01, -4.43814561e-01],\n",
       "       ...,\n",
       "       [ 1.03935979e+00,  9.71535619e-01,  9.39778015e-01, ...,\n",
       "        -7.76203761e-01, -8.36132291e-01,  2.72707990e-01],\n",
       "       [-1.29004025e+00, -1.48715508e+00, -7.71669949e-01, ...,\n",
       "        -1.80478708e+00, -1.70232061e+00, -1.03792910e+00],\n",
       "       [ 7.48184781e-01,  9.71535619e-01,  1.06228218e+00, ...,\n",
       "        -3.36249553e-01,  1.26941899e+00,  1.50272438e+00]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc_X = StandardScaler()\n",
    "X_train_t = sc_X.fit_transform(X_训练集)\n",
    "X_train_t "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "979a1792-4dd0-49d7-b46c-02875e45c3e7",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.6927035 , -1.65654703,  0.76079468,  0.3460357 ,  0.6831717 ,\n",
       "         0.74218223,  2.78969829,  1.98692464,  0.45110059, -0.54727384,\n",
       "        -0.91354476, -1.2691142 , -1.06637576, -0.61301878],\n",
       "       [ 0.40152502,  0.11665824,  0.60238739, -0.09054594, -0.2666013 ,\n",
       "        -0.30707466,  0.00845318,  0.07398091,  0.23597828,  0.3680454 ,\n",
       "        -0.71690202, -0.8840936 ,  0.11874576,  0.16314337],\n",
       "       [-0.79517699, -0.76994439, -0.09621509, -1.10665253, -0.6324604 ,\n",
       "        -0.17984004, -0.25102002,  0.74359193,  1.4012022 ,  2.67066471,\n",
       "         1.85750649,  1.46826974,  1.08078746,  0.96082327],\n",
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       "        -1.47343278, -0.9648717 , -0.21419043,  0.0713649 , -0.29465704,\n",
       "         1.24249412,  1.72465938,  1.43312402,  1.16413627],\n",
       "       [-0.79517699, -0.76994439, -0.58503577, -0.26252643,  0.13567797,\n",
       "         0.51743438,  0.34410857,  0.90229201,  1.39269653,  2.20930665,\n",
       "         0.97324158, -0.3623129 , -0.89637896,  0.12077029],\n",
       "       [ 1.59822703,  1.88986351,  0.24868917,  0.84081541,  1.19991099,\n",
       "         1.56745446,  1.07001281, -0.20411497, -0.45417013,  0.39538229,\n",
       "        -0.36440713, -0.29916963,  0.51616174,  0.65114545],\n",
       "       [-1.0943525 , -0.76994439, -1.23406513, -0.69665716, -0.09403779,\n",
       "        -0.09346337,  0.13491671,  0.85356614,  1.26451618,  1.53308036,\n",
       "        -0.52682725, -0.73264733, -0.74791831, -0.46086925],\n",
       "       [-1.6927035 , -1.65654703, -0.78511747, -0.78248698,  0.29038633,\n",
       "         0.50348973,  1.2429407 ,  1.53859707, -0.84807532, -1.62315739,\n",
       "        -1.39443678, -1.05135449, -1.12530233, -0.97109566],\n",
       "       [-1.393528  , -1.65654703, -0.40788172,  0.05236237, -0.19220941,\n",
       "         0.0719978 ,  0.7804855 ,  1.19415445,  2.90941159,  0.26306522,\n",
       "        -0.68333337, -1.70244889, -1.01446433, -1.39143962],\n",
       "       [ 0.40152502,  0.11665824,  1.48251157, -0.94335239, -1.41076673,\n",
       "        -1.19749182, -0.71488952, -0.83796395, -0.57384601, -0.39976703,\n",
       "        -0.81551763, -0.66894402,  0.50531084,  0.94110721],\n",
       "       [-1.0943525 , -0.76994439,  0.24684137,  0.51019849,  1.09593986,\n",
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       "       [ 1.29905153,  1.00326088,  0.12281249,  0.659413  ,  1.12216243,\n",
       "         2.30624915,  0.24553685, -0.67041283, -1.14224771, -0.73331279,\n",
       "        -1.14931829, -1.28102679, -0.62644747, -0.76157934],\n",
       "       [-0.79517699, -0.76994439, -0.9547409 , -0.72041685, -0.35499019,\n",
       "        -0.00415675, -0.15669336,  0.143831  ,  0.83592216,  1.38378102,\n",
       "         2.80425726,  0.09156108, -0.95148209, -1.58622784],\n",
       "       [-0.19682599,  0.11665824, -1.5190204 , -1.28048775, -0.70612761,\n",
       "        -0.74320933, -0.67513496, -0.42779813, -0.63537193, -0.4354256 ,\n",
       "         0.71391703,  1.16701169,  1.40768041, -0.19518244],\n",
       "       [-1.393528  , -1.65654703,  0.10851577,  0.6349337 ,  0.26886727,\n",
       "         0.70176558,  1.35536879,  1.94488467,  1.11632931, -0.15379827,\n",
       "        -0.63048243,  0.2356257 , -0.6189651 , -0.35026144],\n",
       "       [-1.6927035 , -1.65654703,  0.52480008, -0.03651274,  0.44864707,\n",
       "         1.71632776,  1.7252409 ,  1.53208419, -1.01469505, -0.84223246,\n",
       "        -0.96948791, -0.81506582, -0.55416527, -0.05442853],\n",
       "       [ 0.40152502,  0.11665824,  2.74598155,  0.16630714, -0.60496043,\n",
       "        -1.14632112, -0.6660866 , -1.13873255, -0.7992693 , -0.32202646,\n",
       "        -0.55499468, -0.88613036, -0.46815775,  1.3925029 ],\n",
       "       [-1.393528  , -1.65654703, -0.75394951, -0.10617186, -0.03532166,\n",
       "         0.0065667 ,  0.92825817,  1.14654236,  1.44320606, -0.49359233,\n",
       "        -0.5271367 , -0.68566374, -0.52183883, -0.91360649],\n",
       "       [ 0.10234951,  0.11665824, -0.53029228, -0.43246381, -0.45048217,\n",
       "        -0.33223249,  0.34220156,  0.54579246,  1.28332477,  0.4788901 ,\n",
       "         0.35744774,  1.40241504,  1.04487936,  1.09081855],\n",
       "       [ 0.40152502,  0.11665824,  1.23503706,  0.56287325, -0.79250274,\n",
       "        -0.67795333, -0.5950788 , -1.16416545,  0.05973528,  0.20981214,\n",
       "        -0.21054296,  1.58859845,  1.28089838,  1.05028677],\n",
       "       [ 0.70070052,  1.00326088,  1.56985052,  1.19549697,  0.57420531,\n",
       "        -0.77220073, -0.69450807, -0.66054573, -0.98703903, -0.02819349,\n",
       "         0.10965731, -0.35170345,  1.22271326,  1.37157208],\n",
       "       [-0.19682599,  0.11665824, -1.86916648, -2.24580473, -1.98112089,\n",
       "        -1.508799  , -0.79499322, -0.66451021, -0.82877688,  0.01861054,\n",
       "        -0.0061846 ,  1.0507917 ,  0.26835771, -0.38034057],\n",
       "       [ 0.10234951,  0.11665824,  0.01700778, -0.56870133, -0.78992995,\n",
       "        -0.36347972, -0.1773736 ,  0.09458785,  0.12709652,  0.41490539,\n",
       "         1.15795063,  1.88740503,  2.29151951,  0.47979534],\n",
       "       [ 0.10234951,  0.11665824, -0.51762402, -0.47212544, -0.42320278,\n",
       "        -0.2602115 , -0.62028536, -0.75280573, -0.62003031, -0.86193488,\n",
       "         0.27844611,  0.67644502,  0.95399804,  1.43183092],\n",
       "       [ 1.29905153,  1.00326088,  0.43789615,  1.97399196,  1.54044363,\n",
       "         1.22469664, -1.13032248, -0.8301747 , -0.78615239, -0.56963211,\n",
       "        -0.30225582,  0.0367102 ,  0.44976159,  0.09719787],\n",
       "       [-0.79517699, -0.76994439, -2.11928241, -1.62819044, -0.77987722,\n",
       "        -0.59918666, -0.93835737, -0.04732697,  0.15719403,  1.14407677,\n",
       "         0.48664872, -0.33332058, -1.97548437, -2.14356936],\n",
       "       [ 0.70070052,  1.00326088,  0.76083804, -0.00373152, -1.64012648,\n",
       "        -1.41412978, -0.5583994 ,  0.08784993, -0.07393873,  0.24520975,\n",
       "         0.53715021, -0.17027682,  0.2285306 ,  2.04933419],\n",
       "       [ 0.99987602,  1.00326088, -0.00752821,  0.90932804,  0.48420782,\n",
       "        -0.19680305, -1.59808992, -1.8519831 , -1.52611968, -1.05987624,\n",
       "        -0.22938546, -0.21126705, -0.60062653,  1.14338755],\n",
       "       [ 1.29905153,  1.00326088,  1.21585559,  1.43130654,  1.52420217,\n",
       "         1.01932113,  0.67575381, -0.87287975, -0.59260887, -0.65570332,\n",
       "        -1.17462439, -0.22555652, -0.12469885, -0.39653166],\n",
       "       [ 0.99987602,  1.00326088, -0.21678069,  1.39633599,  1.363676  ,\n",
       "        -0.89023977, -1.08314271, -1.31584972, -0.89959324, -1.30173071,\n",
       "        -0.76803905, -0.26020587, -0.31350165, -0.12206113],\n",
       "       [ 1.59822703,  1.88986351,  0.34210396,  0.67197042,  0.89690201,\n",
       "         2.19754439,  2.06852193,  0.31175681, -0.40023047, -0.92096927,\n",
       "        -0.97298204, -1.05318842, -0.67152199, -0.1356799 ],\n",
       "       [-0.19682599,  0.11665824, -1.62200637, -0.57474657,  0.09740787,\n",
       "        -0.23597512,  0.22920022,  0.55618595,  0.10472519,  0.58422483,\n",
       "         2.09732731,  1.85242154,  1.33342385, -1.44818859],\n",
       "       [-0.19682599,  0.11665824, -0.47623587, -0.46304403, -0.30168604,\n",
       "         0.17763784,  0.6186093 ,  1.16584509,  0.5090705 ,  0.48365522,\n",
       "         1.24726476,  1.38336429,  1.0046959 , -0.15341327],\n",
       "       [ 0.40152502,  0.11665824,  0.47532162, -0.16280658, -1.51926965,\n",
       "        -1.58517296, -1.65838104, -1.02809922, -0.06138274, -0.01506924,\n",
       "        -0.3468181 ,  1.39667473,  1.34060906,  1.52630598],\n",
       "       [-0.79517699, -0.76994439, -0.77743186, -0.77309424, -0.66952651,\n",
       "        -0.40263913, -0.73866199, -0.46022591,  0.86237722,  1.25081976,\n",
       "         1.57940464, -0.12309758, -1.58482578, -1.58968093],\n",
       "       [ 0.70070052,  1.00326088,  1.14672887,  2.63288217,  0.17811458,\n",
       "        -0.60408775, -1.26202375, -0.73112351, -0.96225719, -0.99759698,\n",
       "        -0.37158367, -0.76616766, -0.92162117, -0.40630365],\n",
       "       [ 1.29905153,  1.00326088,  0.68471864,  0.88595724,  2.52046475,\n",
       "         1.60236122,  0.38314773, -0.56194033, -0.73136822, -0.98665578,\n",
       "        -0.79353106, -0.59749645, -0.20192084, -0.42770462]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_t = sc_X.fit_transform(X_测试集)\n",
    "X_test_t"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56189774",
   "metadata": {},
   "source": [
    "## 步骤5：训练和验证模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "efe344f5-3527-4e8d-b62c-489b2735a685",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import Input\n",
    "inputs = Input(shape=(14,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4ae6faeb-bc9d-4f08-8173-3257b40e40c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import Dense\n",
    "x = Dense(7, activation='relu')(inputs)\n",
    "x = Dense(7, activation='relu')(x)\n",
    "x = Dense(7, activation='relu')(x)\n",
    "x = Dense(4, activation='relu')(x)\n",
    "x = Dense(4, activation='relu')(x)\n",
    "x = Dense(4, activation='relu')(x)\n",
    "x = Dense(4, activation='relu')(x)\n",
    "predictions = Dense(units=4, activation='linear')(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "204f8b3c-16c1-4cf1-97d7-bdd58694bd5b",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n",
      "4/4 [==============================] - 0s 55ms/step - loss: 4275169536.0000 - mae: 63497.3594 - val_loss: 3942541056.0000 - val_mae: 60954.7852\n",
      "Epoch 2/1000\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 4275166976.0000 - mae: 63497.3398 - val_loss: 3942538496.0000 - val_mae: 60954.7656\n",
      "Epoch 3/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4275164416.0000 - mae: 63497.3164 - val_loss: 3942536192.0000 - val_mae: 60954.7500\n",
      "Epoch 4/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 4275161856.0000 - mae: 63497.3008 - val_loss: 3942533120.0000 - val_mae: 60954.7227\n",
      "Epoch 5/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4275158528.0000 - mae: 63497.2734 - val_loss: 3942530048.0000 - val_mae: 60954.6953\n",
      "Epoch 6/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4275155200.0000 - mae: 63497.2500 - val_loss: 3942526208.0000 - val_mae: 60954.6602\n",
      "Epoch 7/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4275151360.0000 - mae: 63497.2148 - val_loss: 3942522624.0000 - val_mae: 60954.6289\n",
      "Epoch 8/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4275147008.0000 - mae: 63497.1875 - val_loss: 3942517760.0000 - val_mae: 60954.5898\n",
      "Epoch 9/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4275142144.0000 - mae: 63497.1406 - val_loss: 3942512896.0000 - val_mae: 60954.5508\n",
      "Epoch 10/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4275136768.0000 - mae: 63497.1016 - val_loss: 3942507264.0000 - val_mae: 60954.5000\n",
      "Epoch 11/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4275129600.0000 - mae: 63497.0469 - val_loss: 3942500352.0000 - val_mae: 60954.4414\n",
      "Epoch 12/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4275121920.0000 - mae: 63496.9844 - val_loss: 3942492416.0000 - val_mae: 60954.3750\n",
      "Epoch 13/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 4275113216.0000 - mae: 63496.9180 - val_loss: 3942483200.0000 - val_mae: 60954.2969\n",
      "Epoch 14/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4275102976.0000 - mae: 63496.8320 - val_loss: 3942472960.0000 - val_mae: 60954.2109\n",
      "Epoch 15/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4275091200.0000 - mae: 63496.7422 - val_loss: 3942460672.0000 - val_mae: 60954.1055\n",
      "Epoch 16/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4275077120.0000 - mae: 63496.6328 - val_loss: 3942446592.0000 - val_mae: 60953.9844\n",
      "Epoch 17/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4275061248.0000 - mae: 63496.5078 - val_loss: 3942429696.0000 - val_mae: 60953.8477\n",
      "Epoch 18/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4275042048.0000 - mae: 63496.3516 - val_loss: 3942409984.0000 - val_mae: 60953.6758\n",
      "Epoch 19/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4275020032.0000 - mae: 63496.1836 - val_loss: 3942386944.0000 - val_mae: 60953.4844\n",
      "Epoch 20/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 4274992896.0000 - mae: 63495.9727 - val_loss: 3942359808.0000 - val_mae: 60953.2500\n",
      "Epoch 21/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4274962688.0000 - mae: 63495.7266 - val_loss: 3942327296.0000 - val_mae: 60952.9766\n",
      "Epoch 22/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4274925824.0000 - mae: 63495.4336 - val_loss: 3942288896.0000 - val_mae: 60952.6523\n",
      "Epoch 23/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4274881024.0000 - mae: 63495.0859 - val_loss: 3942244352.0000 - val_mae: 60952.2734\n",
      "Epoch 24/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4274831360.0000 - mae: 63494.6875 - val_loss: 3942190592.0000 - val_mae: 60951.8242\n",
      "Epoch 25/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4274770432.0000 - mae: 63494.2070 - val_loss: 3942127872.0000 - val_mae: 60951.2891\n",
      "Epoch 26/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4274700032.0000 - mae: 63493.6484 - val_loss: 3942053632.0000 - val_mae: 60950.6602\n",
      "Epoch 27/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4274615040.0000 - mae: 63492.9844 - val_loss: 3941966336.0000 - val_mae: 60949.9297\n",
      "Epoch 28/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4274515712.0000 - mae: 63492.2109 - val_loss: 3941863936.0000 - val_mae: 60949.0664\n",
      "Epoch 29/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4274396416.0000 - mae: 63491.2734 - val_loss: 3941742848.0000 - val_mae: 60948.0469\n",
      "Epoch 30/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4274262784.0000 - mae: 63490.2031 - val_loss: 3941600256.0000 - val_mae: 60946.8477\n",
      "Epoch 31/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4274097408.0000 - mae: 63488.9180 - val_loss: 3941433088.0000 - val_mae: 60945.4414\n",
      "Epoch 32/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4273908224.0000 - mae: 63487.4141 - val_loss: 3941236224.0000 - val_mae: 60943.7812\n",
      "Epoch 33/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4273681152.0000 - mae: 63485.6211 - val_loss: 3941005568.0000 - val_mae: 60941.8477\n",
      "Epoch 34/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4273415936.0000 - mae: 63483.5586 - val_loss: 3940735232.0000 - val_mae: 60939.5781\n",
      "Epoch 35/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 4273101824.0000 - mae: 63481.0977 - val_loss: 3940418560.0000 - val_mae: 60936.9180\n",
      "Epoch 36/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4272745728.0000 - mae: 63478.2773 - val_loss: 3940046592.0000 - val_mae: 60933.8047\n",
      "Epoch 37/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4272324352.0000 - mae: 63474.9727 - val_loss: 3939613184.0000 - val_mae: 60930.1680\n",
      "Epoch 38/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4271810048.0000 - mae: 63470.9336 - val_loss: 3939111168.0000 - val_mae: 60925.9648\n",
      "Epoch 39/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4271229184.0000 - mae: 63466.4102 - val_loss: 3938523904.0000 - val_mae: 60921.0469\n",
      "Epoch 40/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4270574848.0000 - mae: 63461.2656 - val_loss: 3937837312.0000 - val_mae: 60915.2930\n",
      "Epoch 41/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4269762304.0000 - mae: 63454.8516 - val_loss: 3937045760.0000 - val_mae: 60908.6680\n",
      "Epoch 42/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 4268866048.0000 - mae: 63447.8516 - val_loss: 3936124928.0000 - val_mae: 60900.9648\n",
      "Epoch 43/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4267804160.0000 - mae: 63439.5547 - val_loss: 3935063040.0000 - val_mae: 60892.0742\n",
      "Epoch 44/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4266586368.0000 - mae: 63429.9922 - val_loss: 3933833216.0000 - val_mae: 60881.7852\n",
      "Epoch 45/1000\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 4265175808.0000 - mae: 63418.8164 - val_loss: 3932413184.0000 - val_mae: 60869.9180\n",
      "Epoch 46/1000\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 4263518464.0000 - mae: 63405.9453 - val_loss: 3930780416.0000 - val_mae: 60856.2734\n",
      "Epoch 47/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4261636864.0000 - mae: 63391.1719 - val_loss: 3928893184.0000 - val_mae: 60840.5000\n",
      "Epoch 48/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4259514880.0000 - mae: 63374.4922 - val_loss: 3926719744.0000 - val_mae: 60822.3477\n",
      "Epoch 49/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4257007104.0000 - mae: 63354.8945 - val_loss: 3924242944.0000 - val_mae: 60801.6523\n",
      "Epoch 50/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4254146048.0000 - mae: 63332.4180 - val_loss: 3921409536.0000 - val_mae: 60777.9922\n",
      "Epoch 51/1000\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 4250831872.0000 - mae: 63306.5664 - val_loss: 3918179584.0000 - val_mae: 60751.0117\n",
      "Epoch 52/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4247122944.0000 - mae: 63277.2266 - val_loss: 3914474240.0000 - val_mae: 60720.0469\n",
      "Epoch 53/1000\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 4242849280.0000 - mae: 63243.8008 - val_loss: 3910248704.0000 - val_mae: 60684.7188\n",
      "Epoch 54/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4237943296.0000 - mae: 63205.3125 - val_loss: 3905440768.0000 - val_mae: 60644.4922\n",
      "Epoch 55/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4232529408.0000 - mae: 63162.8750 - val_loss: 3899949568.0000 - val_mae: 60598.5391\n",
      "Epoch 56/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4226140928.0000 - mae: 63112.0391 - val_loss: 3893760512.0000 - val_mae: 60546.7266\n",
      "Epoch 57/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4219114240.0000 - mae: 63057.1094 - val_loss: 3886719232.0000 - val_mae: 60487.7227\n",
      "Epoch 58/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4210905600.0000 - mae: 62991.9766 - val_loss: 3878784000.0000 - val_mae: 60421.1367\n",
      "Epoch 59/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4202058752.0000 - mae: 62921.8828 - val_loss: 3869757696.0000 - val_mae: 60345.2734\n",
      "Epoch 60/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4191702784.0000 - mae: 62839.9336 - val_loss: 3859628544.0000 - val_mae: 60259.9922\n",
      "Epoch 61/1000\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 4179907328.0000 - mae: 62747.0117 - val_loss: 3848271360.0000 - val_mae: 60164.1523\n",
      "Epoch 62/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 4166905600.0000 - mae: 62644.3945 - val_loss: 3835470848.0000 - val_mae: 60055.8828\n",
      "Epoch 63/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4152512000.0000 - mae: 62528.6328 - val_loss: 3821064448.0000 - val_mae: 59933.7891\n",
      "Epoch 64/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4136287232.0000 - mae: 62399.0703 - val_loss: 3804945152.0000 - val_mae: 59796.7734\n",
      "Epoch 65/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4117893888.0000 - mae: 62251.0078 - val_loss: 3787019008.0000 - val_mae: 59643.9922\n",
      "Epoch 66/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4097741824.0000 - mae: 62089.1680 - val_loss: 3767072512.0000 - val_mae: 59473.3047\n",
      "Epoch 67/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4074650624.0000 - mae: 61903.6367 - val_loss: 3744953856.0000 - val_mae: 59283.3477\n",
      "Epoch 68/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 4049234432.0000 - mae: 61697.4414 - val_loss: 3720339456.0000 - val_mae: 59071.0352\n",
      "Epoch 69/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 4021438720.0000 - mae: 61473.2148 - val_loss: 3692881408.0000 - val_mae: 58832.8633\n",
      "Epoch 70/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 3990138624.0000 - mae: 61216.3008 - val_loss: 3662500608.0000 - val_mae: 58567.8359\n",
      "Epoch 71/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 3955563008.0000 - mae: 60931.8555 - val_loss: 3628914432.0000 - val_mae: 58273.0664\n",
      "Epoch 72/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 3917922048.0000 - mae: 60619.1289 - val_loss: 3591784960.0000 - val_mae: 57945.0234\n",
      "Epoch 73/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 3875675648.0000 - mae: 60265.8594 - val_loss: 3551060480.0000 - val_mae: 57582.3711\n",
      "Epoch 74/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 3829891584.0000 - mae: 59882.4492 - val_loss: 3506298368.0000 - val_mae: 57180.0508\n",
      "Epoch 75/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 3779065088.0000 - mae: 59452.9961 - val_loss: 3457377024.0000 - val_mae: 56736.0898\n",
      "Epoch 76/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 3723499776.0000 - mae: 58977.1328 - val_loss: 3404113152.0000 - val_mae: 56247.2148\n",
      "Epoch 77/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 3663627776.0000 - mae: 58456.6758 - val_loss: 3346071296.0000 - val_mae: 55708.3906\n",
      "Epoch 78/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 3597043968.0000 - mae: 57875.1406 - val_loss: 3283353344.0000 - val_mae: 55118.2461\n",
      "Epoch 79/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 3528163840.0000 - mae: 57259.2695 - val_loss: 3215041280.0000 - val_mae: 54465.6797\n",
      "Epoch 80/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 3450271232.0000 - mae: 56554.2734 - val_loss: 3141902080.0000 - val_mae: 53755.4961\n",
      "Epoch 81/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 3369068800.0000 - mae: 55807.2422 - val_loss: 3063076096.0000 - val_mae: 52975.4453\n",
      "Epoch 82/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 3279200256.0000 - mae: 54974.5156 - val_loss: 2979256576.0000 - val_mae: 52128.6523\n",
      "Epoch 83/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 3183545600.0000 - mae: 54053.5586 - val_loss: 2890294528.0000 - val_mae: 51209.7734\n",
      "Epoch 84/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 3082374656.0000 - mae: 53070.7344 - val_loss: 2795779072.0000 - val_mae: 50208.2148\n",
      "Epoch 85/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 2976980736.0000 - mae: 52028.1914 - val_loss: 2695806208.0000 - val_mae: 49117.9414\n",
      "Epoch 86/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 2865361664.0000 - mae: 50865.2031 - val_loss: 2590796544.0000 - val_mae: 47936.5977\n",
      "Epoch 87/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 2746767104.0000 - mae: 49614.9492 - val_loss: 2481890048.0000 - val_mae: 46669.8438\n",
      "Epoch 88/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 2625707264.0000 - mae: 48258.4844 - val_loss: 2369078272.0000 - val_mae: 45369.8281\n",
      "Epoch 89/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 2497210112.0000 - mae: 46812.9961 - val_loss: 2254096128.0000 - val_mae: 44042.4766\n",
      "Epoch 90/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 2367728384.0000 - mae: 45247.4102 - val_loss: 2137016832.0000 - val_mae: 42624.9180\n",
      "Epoch 91/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 2237802240.0000 - mae: 43705.4844 - val_loss: 2018647936.0000 - val_mae: 41166.9844\n",
      "Epoch 92/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 2105833600.0000 - mae: 42061.1953 - val_loss: 1900722048.0000 - val_mae: 39640.3789\n",
      "Epoch 93/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 1973734912.0000 - mae: 40310.0820 - val_loss: 1784656768.0000 - val_mae: 38143.8047\n",
      "Epoch 94/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 1842049792.0000 - mae: 38606.9141 - val_loss: 1672041088.0000 - val_mae: 36600.0820\n",
      "Epoch 95/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 1718111488.0000 - mae: 36936.8750 - val_loss: 1563769984.0000 - val_mae: 35092.9492\n",
      "Epoch 96/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 1596786048.0000 - mae: 35336.2695 - val_loss: 1461813888.0000 - val_mae: 33590.9961\n",
      "Epoch 97/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 1484355840.0000 - mae: 33793.0078 - val_loss: 1366902272.0000 - val_mae: 32067.9883\n",
      "Epoch 98/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 1375651840.0000 - mae: 32270.6484 - val_loss: 1281469312.0000 - val_mae: 30643.4980\n",
      "Epoch 99/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 1279904128.0000 - mae: 30830.9199 - val_loss: 1204694016.0000 - val_mae: 29428.3340\n",
      "Epoch 100/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 1192616832.0000 - mae: 29439.0801 - val_loss: 1137017984.0000 - val_mae: 28376.7246\n",
      "Epoch 101/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 1114185600.0000 - mae: 28142.0312 - val_loss: 1078248576.0000 - val_mae: 27433.9355\n",
      "Epoch 102/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 1047333504.0000 - mae: 26991.9883 - val_loss: 1027454592.0000 - val_mae: 26573.3828\n",
      "Epoch 103/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 989024064.0000 - mae: 25947.7617 - val_loss: 983675136.0000 - val_mae: 25816.6289\n",
      "Epoch 104/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 939328704.0000 - mae: 25013.4199 - val_loss: 945868160.0000 - val_mae: 25211.6094\n",
      "Epoch 105/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 893174144.0000 - mae: 24146.5508 - val_loss: 913135808.0000 - val_mae: 24694.8672\n",
      "Epoch 106/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 856326720.0000 - mae: 23433.6504 - val_loss: 883997312.0000 - val_mae: 24242.8555\n",
      "Epoch 107/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 822928960.0000 - mae: 22797.6758 - val_loss: 857740672.0000 - val_mae: 23840.8926\n",
      "Epoch 108/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 791704704.0000 - mae: 22250.8965 - val_loss: 833470336.0000 - val_mae: 23457.4023\n",
      "Epoch 109/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 765456512.0000 - mae: 21815.8418 - val_loss: 811004096.0000 - val_mae: 23080.9531\n",
      "Epoch 110/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 739564608.0000 - mae: 21404.7012 - val_loss: 789745664.0000 - val_mae: 22712.8613\n",
      "Epoch 111/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 716956608.0000 - mae: 21073.0215 - val_loss: 769906944.0000 - val_mae: 22358.8027\n",
      "Epoch 112/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 695254720.0000 - mae: 20752.8730 - val_loss: 751093312.0000 - val_mae: 22033.5586\n",
      "Epoch 113/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 676174592.0000 - mae: 20457.8379 - val_loss: 732854592.0000 - val_mae: 21730.6973\n",
      "Epoch 114/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 656597760.0000 - mae: 20173.0508 - val_loss: 716081088.0000 - val_mae: 21454.8555\n",
      "Epoch 115/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 639591360.0000 - mae: 19905.8301 - val_loss: 700129152.0000 - val_mae: 21188.9160\n",
      "Epoch 116/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 623957888.0000 - mae: 19672.3496 - val_loss: 685414208.0000 - val_mae: 20929.0527\n",
      "Epoch 117/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 609354560.0000 - mae: 19442.8750 - val_loss: 671545792.0000 - val_mae: 20687.6699\n",
      "Epoch 118/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 595618880.0000 - mae: 19218.0039 - val_loss: 658097024.0000 - val_mae: 20461.2344\n",
      "Epoch 119/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 582240576.0000 - mae: 19005.2852 - val_loss: 645807552.0000 - val_mae: 20259.3945\n",
      "Epoch 120/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 569837440.0000 - mae: 18797.0527 - val_loss: 634408320.0000 - val_mae: 20071.4727\n",
      "Epoch 121/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 558215808.0000 - mae: 18598.5293 - val_loss: 623820416.0000 - val_mae: 19909.5645\n",
      "Epoch 122/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 547567296.0000 - mae: 18406.8984 - val_loss: 613854848.0000 - val_mae: 19764.1289\n",
      "Epoch 123/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 537430208.0000 - mae: 18228.4863 - val_loss: 604397760.0000 - val_mae: 19622.3047\n",
      "Epoch 124/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 527619008.0000 - mae: 18054.6191 - val_loss: 595472896.0000 - val_mae: 19489.4766\n",
      "Epoch 125/1000\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 518474880.0000 - mae: 17886.3887 - val_loss: 586761664.0000 - val_mae: 19355.9297\n",
      "Epoch 126/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 509879904.0000 - mae: 17721.8652 - val_loss: 578110848.0000 - val_mae: 19221.7988\n",
      "Epoch 127/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 501231680.0000 - mae: 17565.3730 - val_loss: 570045632.0000 - val_mae: 19093.6348\n",
      "Epoch 128/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 493038464.0000 - mae: 17410.7480 - val_loss: 562316352.0000 - val_mae: 18968.2910\n",
      "Epoch 129/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 485712576.0000 - mae: 17271.2930 - val_loss: 554974656.0000 - val_mae: 18845.0137\n",
      "Epoch 130/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 478323168.0000 - mae: 17127.7070 - val_loss: 547568576.0000 - val_mae: 18720.7617\n",
      "Epoch 131/1000\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 471325024.0000 - mae: 16988.5527 - val_loss: 540483904.0000 - val_mae: 18602.0000\n",
      "Epoch 132/1000\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 464314240.0000 - mae: 16850.0156 - val_loss: 534099936.0000 - val_mae: 18492.8496\n",
      "Epoch 133/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 457669952.0000 - mae: 16715.8184 - val_loss: 527921600.0000 - val_mae: 18385.5625\n",
      "Epoch 134/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 451924544.0000 - mae: 16590.6953 - val_loss: 521972576.0000 - val_mae: 18277.5957\n",
      "Epoch 135/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 445406336.0000 - mae: 16456.3438 - val_loss: 516225248.0000 - val_mae: 18175.3848\n",
      "Epoch 136/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 439916896.0000 - mae: 16334.8896 - val_loss: 510646464.0000 - val_mae: 18077.1855\n",
      "Epoch 137/1000\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 433806624.0000 - mae: 16207.7549 - val_loss: 505028384.0000 - val_mae: 17976.9551\n",
      "Epoch 138/1000\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 428337024.0000 - mae: 16091.9043 - val_loss: 499435072.0000 - val_mae: 17879.7637\n",
      "Epoch 139/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 423362048.0000 - mae: 15979.2773 - val_loss: 494289344.0000 - val_mae: 17788.7148\n",
      "Epoch 140/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 418055168.0000 - mae: 15866.5811 - val_loss: 489227648.0000 - val_mae: 17696.9375\n",
      "Epoch 141/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 413090176.0000 - mae: 15754.3115 - val_loss: 484363488.0000 - val_mae: 17608.4668\n",
      "Epoch 142/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 408501536.0000 - mae: 15650.1162 - val_loss: 479750848.0000 - val_mae: 17522.4629\n",
      "Epoch 143/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 403815200.0000 - mae: 15540.2959 - val_loss: 475157024.0000 - val_mae: 17436.9648\n",
      "Epoch 144/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 399241600.0000 - mae: 15436.6289 - val_loss: 470969088.0000 - val_mae: 17354.7207\n",
      "Epoch 145/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 394942016.0000 - mae: 15331.4785 - val_loss: 466850752.0000 - val_mae: 17273.2695\n",
      "Epoch 146/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 390847552.0000 - mae: 15232.1602 - val_loss: 462855264.0000 - val_mae: 17192.3320\n",
      "Epoch 147/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 386989472.0000 - mae: 15136.4912 - val_loss: 458899584.0000 - val_mae: 17112.1484\n",
      "Epoch 148/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 382971424.0000 - mae: 15038.3564 - val_loss: 455029024.0000 - val_mae: 17033.9336\n",
      "Epoch 149/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 379054720.0000 - mae: 14946.5430 - val_loss: 450801536.0000 - val_mae: 16955.5234\n",
      "Epoch 150/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 375281792.0000 - mae: 14859.4590 - val_loss: 446888128.0000 - val_mae: 16881.2637\n",
      "Epoch 151/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 371891200.0000 - mae: 14777.6602 - val_loss: 443168512.0000 - val_mae: 16808.4414\n",
      "Epoch 152/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 368442560.0000 - mae: 14691.1816 - val_loss: 439536512.0000 - val_mae: 16733.5684\n",
      "Epoch 153/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 364942304.0000 - mae: 14610.8662 - val_loss: 436315712.0000 - val_mae: 16663.2207\n",
      "Epoch 154/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 361787520.0000 - mae: 14533.8438 - val_loss: 432873152.0000 - val_mae: 16590.7246\n",
      "Epoch 155/1000\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 358779008.0000 - mae: 14461.8232 - val_loss: 429772800.0000 - val_mae: 16519.8477\n",
      "Epoch 156/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 355632032.0000 - mae: 14387.5830 - val_loss: 426592000.0000 - val_mae: 16449.8555\n",
      "Epoch 157/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 352530368.0000 - mae: 14314.1250 - val_loss: 423613248.0000 - val_mae: 16388.7051\n",
      "Epoch 158/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 349753216.0000 - mae: 14244.1211 - val_loss: 420649952.0000 - val_mae: 16328.3584\n",
      "Epoch 159/1000\n",
      "4/4 [==============================] - ETA: 0s - loss: 350593920.0000 - mae: 14522.965 - 0s 8ms/step - loss: 347066720.0000 - mae: 14177.1963 - val_loss: 417587360.0000 - val_mae: 16270.0029\n",
      "Epoch 160/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 344290688.0000 - mae: 14106.1816 - val_loss: 414868736.0000 - val_mae: 16211.3301\n",
      "Epoch 161/1000\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 341704224.0000 - mae: 14043.7920 - val_loss: 412193216.0000 - val_mae: 16153.0605\n",
      "Epoch 162/1000\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 339131456.0000 - mae: 13980.8955 - val_loss: 409486304.0000 - val_mae: 16097.0830\n",
      "Epoch 163/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 336646784.0000 - mae: 13921.2646 - val_loss: 406909248.0000 - val_mae: 16045.1494\n",
      "Epoch 164/1000\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 334552064.0000 - mae: 13864.7344 - val_loss: 404446880.0000 - val_mae: 15991.8223\n",
      "Epoch 165/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 331979232.0000 - mae: 13803.0000 - val_loss: 401581152.0000 - val_mae: 15945.7822\n",
      "Epoch 166/1000\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 329895872.0000 - mae: 13749.2500 - val_loss: 399008704.0000 - val_mae: 15900.8965\n",
      "Epoch 167/1000\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 327641088.0000 - mae: 13695.4854 - val_loss: 396530272.0000 - val_mae: 15857.3740\n",
      "Epoch 168/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 325579136.0000 - mae: 13643.8018 - val_loss: 394104512.0000 - val_mae: 15816.4492\n",
      "Epoch 169/1000\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 323498656.0000 - mae: 13592.7295 - val_loss: 391832864.0000 - val_mae: 15777.8564\n",
      "Epoch 170/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 321611840.0000 - mae: 13542.1260 - val_loss: 389708672.0000 - val_mae: 15740.7402\n",
      "Epoch 171/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 319598400.0000 - mae: 13494.9482 - val_loss: 387730176.0000 - val_mae: 15705.4707\n",
      "Epoch 172/1000\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 317780960.0000 - mae: 13449.9834 - val_loss: 385619264.0000 - val_mae: 15672.1670\n",
      "Epoch 173/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 315992960.0000 - mae: 13401.3857 - val_loss: 383727296.0000 - val_mae: 15638.7129\n",
      "Epoch 174/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 314152576.0000 - mae: 13356.7129 - val_loss: 381776544.0000 - val_mae: 15606.4805\n",
      "Epoch 175/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 312670080.0000 - mae: 13315.5986 - val_loss: 380196320.0000 - val_mae: 15574.5674\n",
      "Epoch 176/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 310683712.0000 - mae: 13266.9180 - val_loss: 378360064.0000 - val_mae: 15544.9395\n",
      "Epoch 177/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 309113376.0000 - mae: 13226.1416 - val_loss: 376664800.0000 - val_mae: 15515.7051\n",
      "Epoch 178/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 307496672.0000 - mae: 13184.9150 - val_loss: 374770464.0000 - val_mae: 15490.6348\n",
      "Epoch 179/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 305951296.0000 - mae: 13145.2754 - val_loss: 372851456.0000 - val_mae: 15466.9668\n",
      "Epoch 180/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 304428864.0000 - mae: 13103.4043 - val_loss: 371183360.0000 - val_mae: 15440.0889\n",
      "Epoch 181/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 302888160.0000 - mae: 13062.0205 - val_loss: 369676160.0000 - val_mae: 15412.9199\n",
      "Epoch 182/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 301455168.0000 - mae: 13023.4795 - val_loss: 368087168.0000 - val_mae: 15390.3359\n",
      "Epoch 183/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 300053984.0000 - mae: 12984.4580 - val_loss: 366624544.0000 - val_mae: 15369.5146\n",
      "Epoch 184/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 298684960.0000 - mae: 12944.5996 - val_loss: 365166560.0000 - val_mae: 15346.0195\n",
      "Epoch 185/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 297396192.0000 - mae: 12908.3076 - val_loss: 363677824.0000 - val_mae: 15321.5625\n",
      "Epoch 186/1000\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 296113568.0000 - mae: 12877.2080 - val_loss: 362166272.0000 - val_mae: 15299.6426\n",
      "Epoch 187/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 294745088.0000 - mae: 12835.5752 - val_loss: 360632704.0000 - val_mae: 15279.2822\n",
      "Epoch 188/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 293514240.0000 - mae: 12801.3330 - val_loss: 359159648.0000 - val_mae: 15261.7148\n",
      "Epoch 189/1000\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 292349440.0000 - mae: 12769.9043 - val_loss: 357605504.0000 - val_mae: 15242.0664\n",
      "Epoch 190/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 291166688.0000 - mae: 12737.5537 - val_loss: 356017824.0000 - val_mae: 15224.3730\n",
      "Epoch 191/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 290008352.0000 - mae: 12707.6270 - val_loss: 354716896.0000 - val_mae: 15207.1250\n",
      "Epoch 192/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 288788064.0000 - mae: 12672.8711 - val_loss: 353352096.0000 - val_mae: 15188.0391\n",
      "Epoch 193/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 287776320.0000 - mae: 12645.6982 - val_loss: 352060544.0000 - val_mae: 15169.9639\n",
      "Epoch 194/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 286679776.0000 - mae: 12618.6416 - val_loss: 350583040.0000 - val_mae: 15152.0391\n",
      "Epoch 195/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 285709472.0000 - mae: 12591.6084 - val_loss: 349291360.0000 - val_mae: 15134.2930\n",
      "Epoch 196/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 284579328.0000 - mae: 12566.0000 - val_loss: 348252928.0000 - val_mae: 15116.7080\n",
      "Epoch 197/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 283527008.0000 - mae: 12538.5586 - val_loss: 347150560.0000 - val_mae: 15098.9795\n",
      "Epoch 198/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 282790496.0000 - mae: 12520.3682 - val_loss: 346063520.0000 - val_mae: 15079.0664\n",
      "Epoch 199/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 281629920.0000 - mae: 12492.6191 - val_loss: 344792960.0000 - val_mae: 15062.0479\n",
      "Epoch 200/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 280771296.0000 - mae: 12471.0410 - val_loss: 343702496.0000 - val_mae: 15044.3379\n",
      "Epoch 201/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 279878720.0000 - mae: 12451.9434 - val_loss: 342381088.0000 - val_mae: 15030.2598\n",
      "Epoch 202/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 278843584.0000 - mae: 12427.9336 - val_loss: 341211808.0000 - val_mae: 15015.5303\n",
      "Epoch 203/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 278144384.0000 - mae: 12410.5410 - val_loss: 339967680.0000 - val_mae: 15000.9639\n",
      "Epoch 204/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 277162848.0000 - mae: 12388.8242 - val_loss: 339042912.0000 - val_mae: 14983.0752\n",
      "Epoch 205/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 276483200.0000 - mae: 12373.4668 - val_loss: 338110784.0000 - val_mae: 14964.6377\n",
      "Epoch 206/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 275522048.0000 - mae: 12353.2246 - val_loss: 336987936.0000 - val_mae: 14947.4258\n",
      "Epoch 207/1000\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 274763360.0000 - mae: 12334.5312 - val_loss: 336101792.0000 - val_mae: 14930.4092\n",
      "Epoch 208/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 273901376.0000 - mae: 12314.4180 - val_loss: 335260768.0000 - val_mae: 14915.0508\n",
      "Epoch 209/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 273069120.0000 - mae: 12298.9482 - val_loss: 334286080.0000 - val_mae: 14901.4004\n",
      "Epoch 210/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 272282272.0000 - mae: 12280.6514 - val_loss: 333431008.0000 - val_mae: 14888.0127\n",
      "Epoch 211/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 271494080.0000 - mae: 12263.6240 - val_loss: 332606176.0000 - val_mae: 14872.7949\n",
      "Epoch 212/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 270709984.0000 - mae: 12246.2373 - val_loss: 331829664.0000 - val_mae: 14853.3379\n",
      "Epoch 213/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 269963552.0000 - mae: 12230.1045 - val_loss: 331016096.0000 - val_mae: 14837.9580\n",
      "Epoch 214/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 269280160.0000 - mae: 12216.5781 - val_loss: 330164608.0000 - val_mae: 14822.5986\n",
      "Epoch 215/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 268517024.0000 - mae: 12198.8252 - val_loss: 329214624.0000 - val_mae: 14808.0225\n",
      "Epoch 216/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 267972640.0000 - mae: 12188.1074 - val_loss: 328217440.0000 - val_mae: 14797.2061\n",
      "Epoch 217/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 267165248.0000 - mae: 12168.9170 - val_loss: 327488352.0000 - val_mae: 14782.5166\n",
      "Epoch 218/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 266402976.0000 - mae: 12154.1162 - val_loss: 326622048.0000 - val_mae: 14767.9053\n",
      "Epoch 219/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 265782256.0000 - mae: 12142.4688 - val_loss: 325808576.0000 - val_mae: 14757.0908\n",
      "Epoch 220/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 265114992.0000 - mae: 12127.3096 - val_loss: 324994240.0000 - val_mae: 14742.7227\n",
      "Epoch 221/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 264455168.0000 - mae: 12114.6650 - val_loss: 324061888.0000 - val_mae: 14729.9160\n",
      "Epoch 222/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 263877360.0000 - mae: 12102.7188 - val_loss: 323159072.0000 - val_mae: 14716.0146\n",
      "Epoch 223/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 263362496.0000 - mae: 12092.7402 - val_loss: 322501344.0000 - val_mae: 14704.2832\n",
      "Epoch 224/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 262532832.0000 - mae: 12075.8955 - val_loss: 321718720.0000 - val_mae: 14695.5332\n",
      "Epoch 225/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 262001328.0000 - mae: 12064.6895 - val_loss: 320900960.0000 - val_mae: 14683.0957\n",
      "Epoch 226/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 261369104.0000 - mae: 12052.2617 - val_loss: 320168064.0000 - val_mae: 14672.2119\n",
      "Epoch 227/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 260788368.0000 - mae: 12040.0137 - val_loss: 319516288.0000 - val_mae: 14659.5283\n",
      "Epoch 228/1000\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 260228880.0000 - mae: 12026.8613 - val_loss: 318711488.0000 - val_mae: 14649.8086\n",
      "Epoch 229/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 259607968.0000 - mae: 12014.8691 - val_loss: 318010688.0000 - val_mae: 14637.6797\n",
      "Epoch 230/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 259129232.0000 - mae: 12004.7432 - val_loss: 317318496.0000 - val_mae: 14623.6484\n",
      "Epoch 231/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 258541632.0000 - mae: 11992.2295 - val_loss: 316699232.0000 - val_mae: 14614.2988\n",
      "Epoch 232/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 258021136.0000 - mae: 11981.4150 - val_loss: 316092960.0000 - val_mae: 14604.0303\n",
      "Epoch 233/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 257438208.0000 - mae: 11968.7598 - val_loss: 315430784.0000 - val_mae: 14594.2168\n",
      "Epoch 234/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 257040192.0000 - mae: 11961.8398 - val_loss: 314589120.0000 - val_mae: 14582.9697\n",
      "Epoch 235/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 256537904.0000 - mae: 11952.0332 - val_loss: 313792768.0000 - val_mae: 14570.7637\n",
      "Epoch 236/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 255911136.0000 - mae: 11938.4814 - val_loss: 313090848.0000 - val_mae: 14556.8330\n",
      "Epoch 237/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 255351328.0000 - mae: 11926.0332 - val_loss: 312452928.0000 - val_mae: 14545.7568\n",
      "Epoch 238/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 254841712.0000 - mae: 11916.9512 - val_loss: 311843456.0000 - val_mae: 14533.3496\n",
      "Epoch 239/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 254308368.0000 - mae: 11907.0498 - val_loss: 311199616.0000 - val_mae: 14520.2783\n",
      "Epoch 240/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 253909856.0000 - mae: 11898.7666 - val_loss: 310626016.0000 - val_mae: 14506.5371\n",
      "Epoch 241/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 253348336.0000 - mae: 11888.4668 - val_loss: 309960864.0000 - val_mae: 14491.5820\n",
      "Epoch 242/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 252972912.0000 - mae: 11880.8154 - val_loss: 309230944.0000 - val_mae: 14475.1211\n",
      "Epoch 243/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 252449008.0000 - mae: 11870.9600 - val_loss: 308481248.0000 - val_mae: 14461.1250\n",
      "Epoch 244/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 252024752.0000 - mae: 11862.4971 - val_loss: 307825376.0000 - val_mae: 14449.7783\n",
      "Epoch 245/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 251610976.0000 - mae: 11854.2217 - val_loss: 307031232.0000 - val_mae: 14435.6846\n",
      "Epoch 246/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 251030736.0000 - mae: 11840.9688 - val_loss: 306439904.0000 - val_mae: 14424.9639\n",
      "Epoch 247/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 250553792.0000 - mae: 11830.6211 - val_loss: 305788384.0000 - val_mae: 14415.1348\n",
      "Epoch 248/1000\n",
      "4/4 [==============================] - ETA: 0s - loss: 292779840.0000 - mae: 13161.154 - 0s 8ms/step - loss: 250211472.0000 - mae: 11822.9512 - val_loss: 305175584.0000 - val_mae: 14405.5020\n",
      "Epoch 249/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 249778224.0000 - mae: 11814.9941 - val_loss: 304630560.0000 - val_mae: 14393.0098\n",
      "Epoch 250/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 249193952.0000 - mae: 11804.6475 - val_loss: 304020320.0000 - val_mae: 14378.8779\n",
      "Epoch 251/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 248846512.0000 - mae: 11796.6855 - val_loss: 303386816.0000 - val_mae: 14364.2188\n",
      "Epoch 252/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 248395312.0000 - mae: 11787.5342 - val_loss: 302817472.0000 - val_mae: 14352.8486\n",
      "Epoch 253/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 247966064.0000 - mae: 11779.4375 - val_loss: 302270624.0000 - val_mae: 14340.1553\n",
      "Epoch 254/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 247703296.0000 - mae: 11775.7334 - val_loss: 301626752.0000 - val_mae: 14328.1553\n",
      "Epoch 255/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 247198688.0000 - mae: 11764.9092 - val_loss: 301002528.0000 - val_mae: 14312.6064\n",
      "Epoch 256/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 246654208.0000 - mae: 11753.7959 - val_loss: 300537984.0000 - val_mae: 14302.5762\n",
      "Epoch 257/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 246272432.0000 - mae: 11747.4746 - val_loss: 300000032.0000 - val_mae: 14289.7559\n",
      "Epoch 258/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 245858832.0000 - mae: 11739.6982 - val_loss: 299474752.0000 - val_mae: 14275.6533\n",
      "Epoch 259/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 245481328.0000 - mae: 11733.0195 - val_loss: 298911968.0000 - val_mae: 14262.7979\n",
      "Epoch 260/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 245100336.0000 - mae: 11725.9678 - val_loss: 298345248.0000 - val_mae: 14249.8389\n",
      "Epoch 261/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 244701648.0000 - mae: 11718.5605 - val_loss: 297821152.0000 - val_mae: 14238.5889\n",
      "Epoch 262/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 244294624.0000 - mae: 11710.6895 - val_loss: 297293952.0000 - val_mae: 14224.4590\n",
      "Epoch 263/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 244018896.0000 - mae: 11704.7773 - val_loss: 296771904.0000 - val_mae: 14212.1016\n",
      "Epoch 264/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 243543472.0000 - mae: 11699.5566 - val_loss: 296265376.0000 - val_mae: 14201.2236\n",
      "Epoch 265/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 243176944.0000 - mae: 11689.1650 - val_loss: 295766304.0000 - val_mae: 14191.1074\n",
      "Epoch 266/1000\n",
      "4/4 [==============================] - 0s 24ms/step - loss: 242886480.0000 - mae: 11682.5752 - val_loss: 295245888.0000 - val_mae: 14180.2373\n",
      "Epoch 267/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 242469072.0000 - mae: 11672.9248 - val_loss: 294698752.0000 - val_mae: 14167.7842\n",
      "Epoch 268/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 242019296.0000 - mae: 11665.5820 - val_loss: 294217504.0000 - val_mae: 14156.0186\n",
      "Epoch 269/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 241712176.0000 - mae: 11660.1250 - val_loss: 293670912.0000 - val_mae: 14142.3740\n",
      "Epoch 270/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 241270192.0000 - mae: 11652.0684 - val_loss: 293219072.0000 - val_mae: 14131.6670\n",
      "Epoch 271/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 240977184.0000 - mae: 11649.8564 - val_loss: 292808544.0000 - val_mae: 14122.0693\n",
      "Epoch 272/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 240575520.0000 - mae: 11644.1289 - val_loss: 292273984.0000 - val_mae: 14108.1035\n",
      "Epoch 273/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 240218064.0000 - mae: 11635.1748 - val_loss: 291813152.0000 - val_mae: 14097.4336\n",
      "Epoch 274/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 239832640.0000 - mae: 11628.6670 - val_loss: 291376960.0000 - val_mae: 14086.3818\n",
      "Epoch 275/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 239594048.0000 - mae: 11625.3486 - val_loss: 290895616.0000 - val_mae: 14074.4482\n",
      "Epoch 276/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 239181040.0000 - mae: 11618.4102 - val_loss: 290373376.0000 - val_mae: 14059.6729\n",
      "Epoch 277/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 238824784.0000 - mae: 11610.4873 - val_loss: 289921888.0000 - val_mae: 14048.9316\n",
      "Epoch 278/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 238549984.0000 - mae: 11605.2441 - val_loss: 289412192.0000 - val_mae: 14035.2529\n",
      "Epoch 279/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 238273072.0000 - mae: 11599.8154 - val_loss: 288959168.0000 - val_mae: 14025.5273\n",
      "Epoch 280/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 237875136.0000 - mae: 11589.2080 - val_loss: 288525728.0000 - val_mae: 14016.6611\n",
      "Epoch 281/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 237538368.0000 - mae: 11581.7002 - val_loss: 288103520.0000 - val_mae: 14005.2451\n",
      "Epoch 282/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 237352032.0000 - mae: 11580.0459 - val_loss: 287692224.0000 - val_mae: 13996.1035\n",
      "Epoch 283/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 236865952.0000 - mae: 11568.0830 - val_loss: 287264832.0000 - val_mae: 13985.5566\n",
      "Epoch 284/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 236637552.0000 - mae: 11566.2871 - val_loss: 286882496.0000 - val_mae: 13976.1572\n",
      "Epoch 285/1000\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 236395616.0000 - mae: 11563.1816 - val_loss: 286419680.0000 - val_mae: 13963.8379\n",
      "Epoch 286/1000\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 235942192.0000 - mae: 11553.7646 - val_loss: 285974080.0000 - val_mae: 13952.1045\n",
      "Epoch 287/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 235696400.0000 - mae: 11549.1279 - val_loss: 285514176.0000 - val_mae: 13941.4072\n",
      "Epoch 288/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 235301552.0000 - mae: 11538.4287 - val_loss: 285065952.0000 - val_mae: 13930.4834\n",
      "Epoch 289/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 235083088.0000 - mae: 11533.3809 - val_loss: 284655936.0000 - val_mae: 13921.0713\n",
      "Epoch 290/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 234796272.0000 - mae: 11528.3564 - val_loss: 284156000.0000 - val_mae: 13906.0264\n",
      "Epoch 291/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 234740048.0000 - mae: 11535.3857 - val_loss: 283744096.0000 - val_mae: 13894.5947\n",
      "Epoch 292/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 234220816.0000 - mae: 11521.9668 - val_loss: 283267520.0000 - val_mae: 13882.0908\n",
      "Epoch 293/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 234076768.0000 - mae: 11517.6689 - val_loss: 282910880.0000 - val_mae: 13876.4453\n",
      "Epoch 294/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 233635072.0000 - mae: 11506.0596 - val_loss: 282515744.0000 - val_mae: 13867.2041\n",
      "Epoch 295/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 233319504.0000 - mae: 11499.9033 - val_loss: 282084704.0000 - val_mae: 13855.8936\n",
      "Epoch 296/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 233009776.0000 - mae: 11495.9814 - val_loss: 281635648.0000 - val_mae: 13845.1807\n",
      "Epoch 297/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 232771008.0000 - mae: 11491.7627 - val_loss: 281232576.0000 - val_mae: 13834.8799\n",
      "Epoch 298/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 232482912.0000 - mae: 11488.2979 - val_loss: 280865568.0000 - val_mae: 13826.1328\n",
      "Epoch 299/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 232224544.0000 - mae: 11483.5811 - val_loss: 280459456.0000 - val_mae: 13814.9688\n",
      "Epoch 300/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 231876000.0000 - mae: 11478.6143 - val_loss: 279995744.0000 - val_mae: 13801.7637\n",
      "Epoch 301/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 231617712.0000 - mae: 11474.0029 - val_loss: 279618528.0000 - val_mae: 13793.3037\n",
      "Epoch 302/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 231398160.0000 - mae: 11471.3506 - val_loss: 279218496.0000 - val_mae: 13782.8086\n",
      "Epoch 303/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 231129680.0000 - mae: 11466.9229 - val_loss: 278815904.0000 - val_mae: 13771.7686\n",
      "Epoch 304/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 230893504.0000 - mae: 11461.8750 - val_loss: 278472736.0000 - val_mae: 13765.2861\n",
      "Epoch 305/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 230662976.0000 - mae: 11458.2988 - val_loss: 278126784.0000 - val_mae: 13760.3389\n",
      "Epoch 306/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 230313984.0000 - mae: 11446.8115 - val_loss: 277754336.0000 - val_mae: 13751.6621\n",
      "Epoch 307/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 230077552.0000 - mae: 11440.7666 - val_loss: 277344416.0000 - val_mae: 13741.7480\n",
      "Epoch 308/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 229855296.0000 - mae: 11435.9570 - val_loss: 276956352.0000 - val_mae: 13732.0576\n",
      "Epoch 309/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 229528512.0000 - mae: 11429.1230 - val_loss: 276515040.0000 - val_mae: 13720.2080\n",
      "Epoch 310/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 229222064.0000 - mae: 11426.0967 - val_loss: 276041152.0000 - val_mae: 13706.1230\n",
      "Epoch 311/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 229012272.0000 - mae: 11426.6602 - val_loss: 275577056.0000 - val_mae: 13690.4658\n",
      "Epoch 312/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 228744768.0000 - mae: 11421.9561 - val_loss: 275139776.0000 - val_mae: 13678.2725\n",
      "Epoch 313/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 228590192.0000 - mae: 11419.4834 - val_loss: 274755040.0000 - val_mae: 13670.3232\n",
      "Epoch 314/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 228222608.0000 - mae: 11410.9600 - val_loss: 274339040.0000 - val_mae: 13658.8203\n",
      "Epoch 315/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 228024624.0000 - mae: 11408.1846 - val_loss: 273991808.0000 - val_mae: 13651.2256\n",
      "Epoch 316/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 227738848.0000 - mae: 11404.1602 - val_loss: 273635392.0000 - val_mae: 13643.9609\n",
      "Epoch 317/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 227436272.0000 - mae: 11396.0293 - val_loss: 273352992.0000 - val_mae: 13641.7197\n",
      "Epoch 318/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 227209696.0000 - mae: 11389.3428 - val_loss: 273020320.0000 - val_mae: 13634.2461\n",
      "Epoch 319/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 227079536.0000 - mae: 11384.0967 - val_loss: 272745184.0000 - val_mae: 13630.4775\n",
      "Epoch 320/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 226775360.0000 - mae: 11378.7354 - val_loss: 272312704.0000 - val_mae: 13618.4238\n",
      "Epoch 321/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 226530448.0000 - mae: 11373.9424 - val_loss: 271938848.0000 - val_mae: 13607.0762\n",
      "Epoch 322/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 226299024.0000 - mae: 11370.9053 - val_loss: 271611552.0000 - val_mae: 13599.1553\n",
      "Epoch 323/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 225984736.0000 - mae: 11362.8691 - val_loss: 271291680.0000 - val_mae: 13590.8779\n",
      "Epoch 324/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 225782352.0000 - mae: 11356.8564 - val_loss: 270902592.0000 - val_mae: 13580.1680\n",
      "Epoch 325/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 225536192.0000 - mae: 11352.4414 - val_loss: 270538688.0000 - val_mae: 13569.7314\n",
      "Epoch 326/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 225323520.0000 - mae: 11346.8555 - val_loss: 270226496.0000 - val_mae: 13562.6973\n",
      "Epoch 327/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 225060032.0000 - mae: 11340.8770 - val_loss: 269851616.0000 - val_mae: 13553.3652\n",
      "Epoch 328/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 224902576.0000 - mae: 11343.1191 - val_loss: 269448096.0000 - val_mae: 13542.9121\n",
      "Epoch 329/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 224683248.0000 - mae: 11336.7012 - val_loss: 269176128.0000 - val_mae: 13537.4990\n",
      "Epoch 330/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 224382320.0000 - mae: 11331.0830 - val_loss: 268748992.0000 - val_mae: 13524.0244\n",
      "Epoch 331/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 224248352.0000 - mae: 11330.0420 - val_loss: 268286640.0000 - val_mae: 13509.9697\n",
      "Epoch 332/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 223953376.0000 - mae: 11322.6553 - val_loss: 267993808.0000 - val_mae: 13502.7002\n",
      "Epoch 333/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 223707328.0000 - mae: 11318.2979 - val_loss: 267626352.0000 - val_mae: 13491.5146\n",
      "Epoch 334/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 223473840.0000 - mae: 11312.5244 - val_loss: 267301888.0000 - val_mae: 13484.9619\n",
      "Epoch 335/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 223254416.0000 - mae: 11306.3467 - val_loss: 267060000.0000 - val_mae: 13482.1348\n",
      "Epoch 336/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 223019552.0000 - mae: 11300.3770 - val_loss: 266718688.0000 - val_mae: 13473.8506\n",
      "Epoch 337/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 222761056.0000 - mae: 11295.0342 - val_loss: 266352224.0000 - val_mae: 13464.1064\n",
      "Epoch 338/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 222554304.0000 - mae: 11291.6787 - val_loss: 265965472.0000 - val_mae: 13452.2500\n",
      "Epoch 339/1000\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 222319552.0000 - mae: 11287.3936 - val_loss: 265605392.0000 - val_mae: 13443.4365\n",
      "Epoch 340/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 222024192.0000 - mae: 11281.4062 - val_loss: 265278384.0000 - val_mae: 13434.5195\n",
      "Epoch 341/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 221819168.0000 - mae: 11277.3145 - val_loss: 264952672.0000 - val_mae: 13426.4961\n",
      "Epoch 342/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 221619104.0000 - mae: 11275.6455 - val_loss: 264592608.0000 - val_mae: 13416.9727\n",
      "Epoch 343/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 221355552.0000 - mae: 11269.6562 - val_loss: 264342928.0000 - val_mae: 13412.3164\n",
      "Epoch 344/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 221150144.0000 - mae: 11264.6279 - val_loss: 264109104.0000 - val_mae: 13408.6211\n",
      "Epoch 345/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 220916896.0000 - mae: 11257.8428 - val_loss: 263753232.0000 - val_mae: 13398.3730\n",
      "Epoch 346/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 220673808.0000 - mae: 11252.7998 - val_loss: 263410768.0000 - val_mae: 13389.7725\n",
      "Epoch 347/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 220500272.0000 - mae: 11248.2070 - val_loss: 263151152.0000 - val_mae: 13384.3730\n",
      "Epoch 348/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 220345712.0000 - mae: 11247.0732 - val_loss: 262710800.0000 - val_mae: 13371.7002\n",
      "Epoch 349/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 220045216.0000 - mae: 11240.9160 - val_loss: 262412384.0000 - val_mae: 13363.4795\n",
      "Epoch 350/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 219802832.0000 - mae: 11236.1436 - val_loss: 262133888.0000 - val_mae: 13356.1055\n",
      "Epoch 351/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 219615968.0000 - mae: 11231.3838 - val_loss: 261899840.0000 - val_mae: 13352.3262\n",
      "Epoch 352/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 219403008.0000 - mae: 11225.2051 - val_loss: 261784720.0000 - val_mae: 13352.5439\n",
      "Epoch 353/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 219120192.0000 - mae: 11217.5430 - val_loss: 261513616.0000 - val_mae: 13346.6611\n",
      "Epoch 354/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 218924784.0000 - mae: 11214.2402 - val_loss: 261168000.0000 - val_mae: 13336.7178\n",
      "Epoch 355/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 218726736.0000 - mae: 11209.7871 - val_loss: 260855296.0000 - val_mae: 13328.7705\n",
      "Epoch 356/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 218505200.0000 - mae: 11207.0098 - val_loss: 260518048.0000 - val_mae: 13317.9639\n",
      "Epoch 357/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 218367952.0000 - mae: 11203.2900 - val_loss: 260247280.0000 - val_mae: 13309.0908\n",
      "Epoch 358/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 218067664.0000 - mae: 11193.6445 - val_loss: 259903920.0000 - val_mae: 13298.4863\n",
      "Epoch 359/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 217845824.0000 - mae: 11189.3252 - val_loss: 259478352.0000 - val_mae: 13285.5312\n",
      "Epoch 360/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 217659408.0000 - mae: 11186.9082 - val_loss: 259158192.0000 - val_mae: 13277.0488\n",
      "Epoch 361/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 217436576.0000 - mae: 11182.2148 - val_loss: 258960608.0000 - val_mae: 13273.7070\n",
      "Epoch 362/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 217185936.0000 - mae: 11176.8525 - val_loss: 258604096.0000 - val_mae: 13263.5830\n",
      "Epoch 363/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 217071200.0000 - mae: 11176.2666 - val_loss: 258128800.0000 - val_mae: 13245.6455\n",
      "Epoch 364/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 216738512.0000 - mae: 11169.7207 - val_loss: 257917264.0000 - val_mae: 13240.8369\n",
      "Epoch 365/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 216544368.0000 - mae: 11164.9961 - val_loss: 257614576.0000 - val_mae: 13232.6777\n",
      "Epoch 366/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 216331056.0000 - mae: 11159.9619 - val_loss: 257300480.0000 - val_mae: 13224.0322\n",
      "Epoch 367/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 216176912.0000 - mae: 11155.2529 - val_loss: 257165104.0000 - val_mae: 13222.1875\n",
      "Epoch 368/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 215911024.0000 - mae: 11149.0947 - val_loss: 256935744.0000 - val_mae: 13217.0674\n",
      "Epoch 369/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 215739456.0000 - mae: 11144.2920 - val_loss: 256639760.0000 - val_mae: 13207.5459\n",
      "Epoch 370/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 215614208.0000 - mae: 11143.5791 - val_loss: 256173952.0000 - val_mae: 13193.3740\n",
      "Epoch 371/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 215307152.0000 - mae: 11138.6670 - val_loss: 255856016.0000 - val_mae: 13183.9658\n",
      "Epoch 372/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 215051328.0000 - mae: 11132.2900 - val_loss: 255583088.0000 - val_mae: 13175.5596\n",
      "Epoch 373/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 214889456.0000 - mae: 11128.3818 - val_loss: 255328208.0000 - val_mae: 13167.8496\n",
      "Epoch 374/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 214665232.0000 - mae: 11121.9736 - val_loss: 255110176.0000 - val_mae: 13161.7422\n",
      "Epoch 375/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 214493248.0000 - mae: 11118.6104 - val_loss: 254786288.0000 - val_mae: 13152.1816\n",
      "Epoch 376/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 214358512.0000 - mae: 11115.0957 - val_loss: 254690368.0000 - val_mae: 13150.9014\n",
      "Epoch 377/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 214096864.0000 - mae: 11107.8066 - val_loss: 254412496.0000 - val_mae: 13143.8721\n",
      "Epoch 378/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 213949168.0000 - mae: 11104.2754 - val_loss: 254087872.0000 - val_mae: 13134.1953\n",
      "Epoch 379/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 213743392.0000 - mae: 11099.2188 - val_loss: 253960192.0000 - val_mae: 13131.4990\n",
      "Epoch 380/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 213644736.0000 - mae: 11094.1318 - val_loss: 253779600.0000 - val_mae: 13125.1523\n",
      "Epoch 381/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 213306816.0000 - mae: 11086.5449 - val_loss: 253332000.0000 - val_mae: 13112.2402\n",
      "Epoch 382/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 213147040.0000 - mae: 11084.5459 - val_loss: 252966976.0000 - val_mae: 13099.2705\n",
      "Epoch 383/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 213032432.0000 - mae: 11089.7520 - val_loss: 252470000.0000 - val_mae: 13083.3018\n",
      "Epoch 384/1000\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 212980560.0000 - mae: 11091.7959 - val_loss: 252020688.0000 - val_mae: 13068.9678\n",
      "Epoch 385/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 212587920.0000 - mae: 11084.5879 - val_loss: 251716480.0000 - val_mae: 13058.6475\n",
      "Epoch 386/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 212342976.0000 - mae: 11076.1855 - val_loss: 251603168.0000 - val_mae: 13055.8867\n",
      "Epoch 387/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 212112304.0000 - mae: 11067.5752 - val_loss: 251547968.0000 - val_mae: 13054.9736\n",
      "Epoch 388/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 211920144.0000 - mae: 11057.7500 - val_loss: 251398544.0000 - val_mae: 13051.1416\n",
      "Epoch 389/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 211759920.0000 - mae: 11052.5996 - val_loss: 251241424.0000 - val_mae: 13045.7783\n",
      "Epoch 390/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 211586720.0000 - mae: 11047.5439 - val_loss: 250976592.0000 - val_mae: 13038.6191\n",
      "Epoch 391/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 211387600.0000 - mae: 11044.5977 - val_loss: 250764560.0000 - val_mae: 13032.3145\n",
      "Epoch 392/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 211204832.0000 - mae: 11040.6426 - val_loss: 250410640.0000 - val_mae: 13020.0625\n",
      "Epoch 393/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 211047152.0000 - mae: 11037.5527 - val_loss: 250073440.0000 - val_mae: 13007.3730\n",
      "Epoch 394/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 210815776.0000 - mae: 11033.1475 - val_loss: 249731808.0000 - val_mae: 12996.2891\n",
      "Epoch 395/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 210683120.0000 - mae: 11031.4580 - val_loss: 249619984.0000 - val_mae: 12994.1738\n",
      "Epoch 396/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 210380352.0000 - mae: 11023.6729 - val_loss: 249234112.0000 - val_mae: 12980.3623\n",
      "Epoch 397/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 210227712.0000 - mae: 11022.0312 - val_loss: 248782560.0000 - val_mae: 12965.3008\n",
      "Epoch 398/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 210283008.0000 - mae: 11026.9355 - val_loss: 248080240.0000 - val_mae: 12939.7900\n",
      "Epoch 399/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 209915216.0000 - mae: 11024.1592 - val_loss: 247720320.0000 - val_mae: 12926.3643\n",
      "Epoch 400/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 209799648.0000 - mae: 11020.7715 - val_loss: 247694208.0000 - val_mae: 12927.4004\n",
      "Epoch 401/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 209503376.0000 - mae: 11011.3262 - val_loss: 247346896.0000 - val_mae: 12915.7412\n",
      "Epoch 402/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 209280304.0000 - mae: 11007.9385 - val_loss: 247190112.0000 - val_mae: 12911.5996\n",
      "Epoch 403/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 209139168.0000 - mae: 11002.7285 - val_loss: 246979344.0000 - val_mae: 12905.6475\n",
      "Epoch 404/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 208927024.0000 - mae: 10996.5977 - val_loss: 246879456.0000 - val_mae: 12902.7461\n",
      "Epoch 405/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 208738816.0000 - mae: 10992.9932 - val_loss: 246647712.0000 - val_mae: 12896.7061\n",
      "Epoch 406/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 208489200.0000 - mae: 10986.4150 - val_loss: 246266848.0000 - val_mae: 12882.4941\n",
      "Epoch 407/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 208321856.0000 - mae: 10982.8369 - val_loss: 245913152.0000 - val_mae: 12870.0625\n",
      "Epoch 408/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 208213664.0000 - mae: 10980.1787 - val_loss: 245633616.0000 - val_mae: 12860.1523\n",
      "Epoch 409/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 208007920.0000 - mae: 10976.4951 - val_loss: 245259456.0000 - val_mae: 12847.7441\n",
      "Epoch 410/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 207912496.0000 - mae: 10974.9941 - val_loss: 245187440.0000 - val_mae: 12844.8496\n",
      "Epoch 411/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 207600784.0000 - mae: 10966.2412 - val_loss: 244811168.0000 - val_mae: 12831.3691\n",
      "Epoch 412/1000\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 207560864.0000 - mae: 10965.8691 - val_loss: 244617216.0000 - val_mae: 12823.8301\n",
      "Epoch 413/1000\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 207297504.0000 - mae: 10958.7588 - val_loss: 244176880.0000 - val_mae: 12808.8223\n",
      "Epoch 414/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 207195120.0000 - mae: 10960.6396 - val_loss: 243805152.0000 - val_mae: 12796.6133\n",
      "Epoch 415/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 207033376.0000 - mae: 10952.8936 - val_loss: 243758544.0000 - val_mae: 12796.4014\n",
      "Epoch 416/1000\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 206844496.0000 - mae: 10951.0977 - val_loss: 243324432.0000 - val_mae: 12782.4277\n",
      "Epoch 417/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 206603744.0000 - mae: 10946.7549 - val_loss: 243089520.0000 - val_mae: 12775.7070\n",
      "Epoch 418/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 206436384.0000 - mae: 10942.7031 - val_loss: 242975952.0000 - val_mae: 12774.0127\n",
      "Epoch 419/1000\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 206345872.0000 - mae: 10939.4824 - val_loss: 243051504.0000 - val_mae: 12779.1670\n",
      "Epoch 420/1000\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 206073888.0000 - mae: 10932.1992 - val_loss: 242736000.0000 - val_mae: 12768.0830\n",
      "Epoch 421/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 205909136.0000 - mae: 10927.6113 - val_loss: 242427152.0000 - val_mae: 12757.5283\n",
      "Epoch 422/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 205847312.0000 - mae: 10929.5723 - val_loss: 242071616.0000 - val_mae: 12745.8223\n",
      "Epoch 423/1000\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 205579008.0000 - mae: 10922.0117 - val_loss: 241992384.0000 - val_mae: 12743.6045\n",
      "Epoch 424/1000\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 205390960.0000 - mae: 10917.0010 - val_loss: 241763312.0000 - val_mae: 12735.8320\n",
      "Epoch 425/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 205275648.0000 - mae: 10914.9570 - val_loss: 241632736.0000 - val_mae: 12731.3252\n",
      "Epoch 426/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 205264704.0000 - mae: 10914.2559 - val_loss: 241109408.0000 - val_mae: 12712.5186\n",
      "Epoch 427/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 204925440.0000 - mae: 10907.9365 - val_loss: 240904512.0000 - val_mae: 12704.0029\n",
      "Epoch 428/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 204833856.0000 - mae: 10902.5918 - val_loss: 240793472.0000 - val_mae: 12700.8721\n",
      "Epoch 429/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 204602016.0000 - mae: 10898.4219 - val_loss: 240407344.0000 - val_mae: 12687.8652\n",
      "Epoch 430/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 204472016.0000 - mae: 10896.5449 - val_loss: 240055776.0000 - val_mae: 12676.7373\n",
      "Epoch 431/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 204258624.0000 - mae: 10892.2188 - val_loss: 239862592.0000 - val_mae: 12670.0498\n",
      "Epoch 432/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 204137408.0000 - mae: 10889.4443 - val_loss: 239478976.0000 - val_mae: 12656.6279\n",
      "Epoch 433/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 203989808.0000 - mae: 10886.5000 - val_loss: 239285040.0000 - val_mae: 12648.6084\n",
      "Epoch 434/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 203860128.0000 - mae: 10882.8096 - val_loss: 239006032.0000 - val_mae: 12639.5244\n",
      "Epoch 435/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 203699616.0000 - mae: 10882.9502 - val_loss: 238588656.0000 - val_mae: 12625.5234\n",
      "Epoch 436/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 203593216.0000 - mae: 10878.4395 - val_loss: 238474992.0000 - val_mae: 12620.9404\n",
      "Epoch 437/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 203327680.0000 - mae: 10871.7812 - val_loss: 238222304.0000 - val_mae: 12612.5752\n",
      "Epoch 438/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 203234640.0000 - mae: 10869.6709 - val_loss: 237950416.0000 - val_mae: 12603.8652\n",
      "Epoch 439/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 202987504.0000 - mae: 10863.4053 - val_loss: 237815856.0000 - val_mae: 12599.4316\n",
      "Epoch 440/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 202937664.0000 - mae: 10865.7607 - val_loss: 237461632.0000 - val_mae: 12586.9668\n",
      "Epoch 441/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 202695472.0000 - mae: 10858.3545 - val_loss: 237418832.0000 - val_mae: 12585.6201\n",
      "Epoch 442/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 202973104.0000 - mae: 10863.8730 - val_loss: 237735904.0000 - val_mae: 12596.4316\n",
      "Epoch 443/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 202453088.0000 - mae: 10847.1055 - val_loss: 237615696.0000 - val_mae: 12592.8359\n",
      "Epoch 444/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 202271920.0000 - mae: 10844.7461 - val_loss: 237024320.0000 - val_mae: 12574.1895\n",
      "Epoch 445/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 202068176.0000 - mae: 10840.3096 - val_loss: 236575648.0000 - val_mae: 12558.9287\n",
      "Epoch 446/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 201919696.0000 - mae: 10839.2236 - val_loss: 236074192.0000 - val_mae: 12541.7256\n",
      "Epoch 447/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 201755248.0000 - mae: 10838.2236 - val_loss: 235758208.0000 - val_mae: 12529.9287\n",
      "Epoch 448/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 201657792.0000 - mae: 10838.7607 - val_loss: 235477296.0000 - val_mae: 12520.0674\n",
      "Epoch 449/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 201494896.0000 - mae: 10835.2969 - val_loss: 235355056.0000 - val_mae: 12515.9092\n",
      "Epoch 450/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 201302112.0000 - mae: 10829.3936 - val_loss: 235163712.0000 - val_mae: 12509.5605\n",
      "Epoch 451/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 201147568.0000 - mae: 10825.0205 - val_loss: 235148304.0000 - val_mae: 12508.8486\n",
      "Epoch 452/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 201036368.0000 - mae: 10820.5684 - val_loss: 235106800.0000 - val_mae: 12507.0938\n",
      "Epoch 453/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 200870880.0000 - mae: 10815.0107 - val_loss: 234916960.0000 - val_mae: 12500.2500\n",
      "Epoch 454/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 200746832.0000 - mae: 10812.0938 - val_loss: 234980656.0000 - val_mae: 12502.0137\n",
      "Epoch 455/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 200549056.0000 - mae: 10805.9482 - val_loss: 234625648.0000 - val_mae: 12489.7861\n",
      "Epoch 456/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 200474688.0000 - mae: 10805.4092 - val_loss: 234258832.0000 - val_mae: 12476.2441\n",
      "Epoch 457/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 200309792.0000 - mae: 10805.5566 - val_loss: 233841008.0000 - val_mae: 12462.2139\n",
      "Epoch 458/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 200138416.0000 - mae: 10799.8096 - val_loss: 233839056.0000 - val_mae: 12461.2012\n",
      "Epoch 459/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 199896736.0000 - mae: 10793.8594 - val_loss: 233512864.0000 - val_mae: 12449.5664\n",
      "Epoch 460/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 199800864.0000 - mae: 10792.6025 - val_loss: 233019392.0000 - val_mae: 12431.4248\n",
      "Epoch 461/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 199794976.0000 - mae: 10795.6465 - val_loss: 232629616.0000 - val_mae: 12418.5312\n",
      "Epoch 462/1000\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 199482320.0000 - mae: 10788.9990 - val_loss: 232474768.0000 - val_mae: 12413.2959\n",
      "Epoch 463/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 199341392.0000 - mae: 10785.0537 - val_loss: 232464576.0000 - val_mae: 12412.5205\n",
      "Epoch 464/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 199178528.0000 - mae: 10775.8906 - val_loss: 232563104.0000 - val_mae: 12413.2295\n",
      "Epoch 465/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 198990672.0000 - mae: 10769.8584 - val_loss: 232397648.0000 - val_mae: 12407.9541\n",
      "Epoch 466/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 198862560.0000 - mae: 10765.9727 - val_loss: 232075152.0000 - val_mae: 12395.4561\n",
      "Epoch 467/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 198747632.0000 - mae: 10762.7012 - val_loss: 231953872.0000 - val_mae: 12390.1729\n",
      "Epoch 468/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 198703344.0000 - mae: 10765.0059 - val_loss: 231463280.0000 - val_mae: 12375.0234\n",
      "Epoch 469/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 198386080.0000 - mae: 10759.5107 - val_loss: 231115280.0000 - val_mae: 12361.9668\n",
      "Epoch 470/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 198267488.0000 - mae: 10756.8730 - val_loss: 230911872.0000 - val_mae: 12355.6201\n",
      "Epoch 471/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 198214608.0000 - mae: 10758.9277 - val_loss: 230488384.0000 - val_mae: 12342.6631\n",
      "Epoch 472/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 197981632.0000 - mae: 10755.0371 - val_loss: 230293728.0000 - val_mae: 12335.8379\n",
      "Epoch 473/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 197827408.0000 - mae: 10750.5273 - val_loss: 230149520.0000 - val_mae: 12330.5215\n",
      "Epoch 474/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 197661040.0000 - mae: 10743.6816 - val_loss: 230163728.0000 - val_mae: 12330.4541\n",
      "Epoch 475/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 197543696.0000 - mae: 10738.4170 - val_loss: 230097872.0000 - val_mae: 12326.8379\n",
      "Epoch 476/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 197444464.0000 - mae: 10739.4814 - val_loss: 229678176.0000 - val_mae: 12312.8086\n",
      "Epoch 477/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 197208240.0000 - mae: 10734.0361 - val_loss: 229497728.0000 - val_mae: 12307.7285\n",
      "Epoch 478/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 197086144.0000 - mae: 10733.5615 - val_loss: 229200064.0000 - val_mae: 12297.7959\n",
      "Epoch 479/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 196997856.0000 - mae: 10730.9375 - val_loss: 229045968.0000 - val_mae: 12291.1543\n",
      "Epoch 480/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 197018768.0000 - mae: 10739.3701 - val_loss: 228553760.0000 - val_mae: 12274.5098\n",
      "Epoch 481/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 196830192.0000 - mae: 10729.7881 - val_loss: 228778848.0000 - val_mae: 12281.6914\n",
      "Epoch 482/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 196494624.0000 - mae: 10720.8330 - val_loss: 228507120.0000 - val_mae: 12271.5547\n",
      "Epoch 483/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 196402480.0000 - mae: 10718.3848 - val_loss: 228511872.0000 - val_mae: 12270.0146\n",
      "Epoch 484/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 196241760.0000 - mae: 10713.4072 - val_loss: 228051984.0000 - val_mae: 12253.7061\n",
      "Epoch 485/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 196068016.0000 - mae: 10710.3447 - val_loss: 227903840.0000 - val_mae: 12247.8203\n",
      "Epoch 486/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 195920096.0000 - mae: 10706.8926 - val_loss: 227634752.0000 - val_mae: 12237.6250\n",
      "Epoch 487/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 195774400.0000 - mae: 10704.4502 - val_loss: 227475024.0000 - val_mae: 12231.8271\n",
      "Epoch 488/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 195691344.0000 - mae: 10703.5293 - val_loss: 227155280.0000 - val_mae: 12219.5352\n",
      "Epoch 489/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 195544512.0000 - mae: 10700.7363 - val_loss: 227067840.0000 - val_mae: 12214.8311\n",
      "Epoch 490/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 195375008.0000 - mae: 10695.7246 - val_loss: 226915200.0000 - val_mae: 12209.5713\n",
      "Epoch 491/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 195230160.0000 - mae: 10693.4873 - val_loss: 226544592.0000 - val_mae: 12196.8623\n",
      "Epoch 492/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 195144480.0000 - mae: 10693.8301 - val_loss: 226410576.0000 - val_mae: 12192.3467\n",
      "Epoch 493/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 195053776.0000 - mae: 10692.1562 - val_loss: 225921024.0000 - val_mae: 12174.7402\n",
      "Epoch 494/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 194850880.0000 - mae: 10689.9971 - val_loss: 225720288.0000 - val_mae: 12167.8750\n",
      "Epoch 495/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 194781984.0000 - mae: 10686.6221 - val_loss: 225928624.0000 - val_mae: 12175.6416\n",
      "Epoch 496/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 194732448.0000 - mae: 10684.5664 - val_loss: 225601936.0000 - val_mae: 12163.8418\n",
      "Epoch 497/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 194417712.0000 - mae: 10677.3564 - val_loss: 225707248.0000 - val_mae: 12167.0439\n",
      "Epoch 498/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 194329632.0000 - mae: 10675.0957 - val_loss: 225465232.0000 - val_mae: 12156.9863\n",
      "Epoch 499/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 194157872.0000 - mae: 10669.6289 - val_loss: 225451184.0000 - val_mae: 12155.7910\n",
      "Epoch 500/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 194035504.0000 - mae: 10666.6973 - val_loss: 225294560.0000 - val_mae: 12151.5869\n",
      "Epoch 501/1000\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 193925968.0000 - mae: 10666.9268 - val_loss: 224862816.0000 - val_mae: 12135.8760\n",
      "Epoch 502/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 193799232.0000 - mae: 10664.3135 - val_loss: 224561808.0000 - val_mae: 12124.9814\n",
      "Epoch 503/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 193673632.0000 - mae: 10662.2256 - val_loss: 224547664.0000 - val_mae: 12122.1982\n",
      "Epoch 504/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 193475072.0000 - mae: 10655.5889 - val_loss: 224250656.0000 - val_mae: 12111.1924\n",
      "Epoch 505/1000\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 193345392.0000 - mae: 10654.1182 - val_loss: 224002048.0000 - val_mae: 12103.3867\n",
      "Epoch 506/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 193310816.0000 - mae: 10659.6035 - val_loss: 223467056.0000 - val_mae: 12082.8486\n",
      "Epoch 507/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 193109680.0000 - mae: 10654.2500 - val_loss: 223039184.0000 - val_mae: 12065.7217\n",
      "Epoch 508/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 193022736.0000 - mae: 10651.2363 - val_loss: 223033152.0000 - val_mae: 12063.7900\n",
      "Epoch 509/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 192843840.0000 - mae: 10648.0742 - val_loss: 222793328.0000 - val_mae: 12056.4658\n",
      "Epoch 510/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 192670096.0000 - mae: 10644.4404 - val_loss: 222708080.0000 - val_mae: 12053.4277\n",
      "Epoch 511/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 192538128.0000 - mae: 10640.6191 - val_loss: 222539280.0000 - val_mae: 12046.8086\n",
      "Epoch 512/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 192504720.0000 - mae: 10637.2021 - val_loss: 222722272.0000 - val_mae: 12051.3389\n",
      "Epoch 513/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 192560032.0000 - mae: 10642.3096 - val_loss: 222132416.0000 - val_mae: 12031.2627\n",
      "Epoch 514/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 192237104.0000 - mae: 10630.7334 - val_loss: 222231792.0000 - val_mae: 12031.3135\n",
      "Epoch 515/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 192134928.0000 - mae: 10626.2373 - val_loss: 222260816.0000 - val_mae: 12031.9463\n",
      "Epoch 516/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 191923776.0000 - mae: 10621.5879 - val_loss: 221727456.0000 - val_mae: 12013.0879\n",
      "Epoch 517/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 191779296.0000 - mae: 10621.4365 - val_loss: 221333696.0000 - val_mae: 11998.4258\n",
      "Epoch 518/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 191701024.0000 - mae: 10620.1230 - val_loss: 221226144.0000 - val_mae: 11992.5215\n",
      "Epoch 519/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 191567248.0000 - mae: 10615.9580 - val_loss: 220933200.0000 - val_mae: 11980.8643\n",
      "Epoch 520/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 191390432.0000 - mae: 10615.7041 - val_loss: 220295072.0000 - val_mae: 11959.4414\n",
      "Epoch 521/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 191389024.0000 - mae: 10618.9570 - val_loss: 219893040.0000 - val_mae: 11944.8203\n",
      "Epoch 522/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 191284576.0000 - mae: 10620.9268 - val_loss: 219830912.0000 - val_mae: 11942.6602\n",
      "Epoch 523/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 191036256.0000 - mae: 10612.0029 - val_loss: 219849376.0000 - val_mae: 11941.9102\n",
      "Epoch 524/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 190990752.0000 - mae: 10609.1982 - val_loss: 220109664.0000 - val_mae: 11949.5293\n",
      "Epoch 525/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 190812112.0000 - mae: 10601.2549 - val_loss: 220131952.0000 - val_mae: 11949.1104\n",
      "Epoch 526/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 190754608.0000 - mae: 10600.1621 - val_loss: 220116960.0000 - val_mae: 11948.0029\n",
      "Epoch 527/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 190558576.0000 - mae: 10593.5879 - val_loss: 219678688.0000 - val_mae: 11932.7793\n",
      "Epoch 528/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 190449904.0000 - mae: 10593.5977 - val_loss: 219120560.0000 - val_mae: 11912.8105\n",
      "Epoch 529/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 190362080.0000 - mae: 10595.5293 - val_loss: 218716176.0000 - val_mae: 11899.3623\n",
      "Epoch 530/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 190250864.0000 - mae: 10594.7227 - val_loss: 218690416.0000 - val_mae: 11897.0293\n",
      "Epoch 531/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 190085904.0000 - mae: 10590.2246 - val_loss: 218449008.0000 - val_mae: 11889.0918\n",
      "Epoch 532/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 190023024.0000 - mae: 10592.4404 - val_loss: 218176016.0000 - val_mae: 11878.8604\n",
      "Epoch 533/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 189856608.0000 - mae: 10588.0264 - val_loss: 218160736.0000 - val_mae: 11876.8164\n",
      "Epoch 534/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 189732960.0000 - mae: 10585.2031 - val_loss: 217839664.0000 - val_mae: 11865.8574\n",
      "Epoch 535/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 189590816.0000 - mae: 10580.7119 - val_loss: 217930720.0000 - val_mae: 11868.2100\n",
      "Epoch 536/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 189484576.0000 - mae: 10579.0967 - val_loss: 217992096.0000 - val_mae: 11869.9033\n",
      "Epoch 537/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 189351280.0000 - mae: 10572.5830 - val_loss: 217798768.0000 - val_mae: 11858.7949\n",
      "Epoch 538/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 189456496.0000 - mae: 10577.6699 - val_loss: 217202128.0000 - val_mae: 11837.0918\n",
      "Epoch 539/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 189374832.0000 - mae: 10576.1758 - val_loss: 217600912.0000 - val_mae: 11850.1299\n",
      "Epoch 540/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 189007536.0000 - mae: 10565.3174 - val_loss: 217315120.0000 - val_mae: 11840.3418\n",
      "Epoch 541/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 188870800.0000 - mae: 10562.9287 - val_loss: 216899776.0000 - val_mae: 11824.0352\n",
      "Epoch 542/1000\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 188726976.0000 - mae: 10561.5879 - val_loss: 216692272.0000 - val_mae: 11814.8105\n",
      "Epoch 543/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 188588112.0000 - mae: 10558.4463 - val_loss: 216542176.0000 - val_mae: 11808.1465\n",
      "Epoch 544/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 188476064.0000 - mae: 10554.6396 - val_loss: 216385264.0000 - val_mae: 11801.2256\n",
      "Epoch 545/1000\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 188423888.0000 - mae: 10552.4697 - val_loss: 216308432.0000 - val_mae: 11797.6084\n",
      "Epoch 546/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 188294960.0000 - mae: 10549.0400 - val_loss: 216229456.0000 - val_mae: 11795.5508\n",
      "Epoch 547/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 188233984.0000 - mae: 10548.7070 - val_loss: 215927440.0000 - val_mae: 11786.1484\n",
      "Epoch 548/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 188096288.0000 - mae: 10546.8604 - val_loss: 215904144.0000 - val_mae: 11782.9580\n",
      "Epoch 549/1000\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 188013984.0000 - mae: 10545.9092 - val_loss: 215819056.0000 - val_mae: 11780.7520\n",
      "Epoch 550/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 187962160.0000 - mae: 10544.7646 - val_loss: 215347280.0000 - val_mae: 11762.9990\n",
      "Epoch 551/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 187807744.0000 - mae: 10544.9268 - val_loss: 215149472.0000 - val_mae: 11752.3809\n",
      "Epoch 552/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 187716128.0000 - mae: 10543.4189 - val_loss: 215127376.0000 - val_mae: 11753.0693\n",
      "Epoch 553/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 187578688.0000 - mae: 10537.9648 - val_loss: 215238992.0000 - val_mae: 11755.4209\n",
      "Epoch 554/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 187609184.0000 - mae: 10536.2920 - val_loss: 215411232.0000 - val_mae: 11757.3955\n",
      "Epoch 555/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 187335232.0000 - mae: 10528.6406 - val_loss: 214933664.0000 - val_mae: 11738.4395\n",
      "Epoch 556/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 187222528.0000 - mae: 10527.4287 - val_loss: 214482848.0000 - val_mae: 11719.7539\n",
      "Epoch 557/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 187134544.0000 - mae: 10528.4443 - val_loss: 214205520.0000 - val_mae: 11708.5215\n",
      "Epoch 558/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 187060016.0000 - mae: 10531.1123 - val_loss: 213726256.0000 - val_mae: 11691.1191\n",
      "Epoch 559/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 187074480.0000 - mae: 10536.7090 - val_loss: 213315824.0000 - val_mae: 11673.7275\n",
      "Epoch 560/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 186938448.0000 - mae: 10534.1934 - val_loss: 213198832.0000 - val_mae: 11670.6650\n",
      "Epoch 561/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 186743120.0000 - mae: 10524.2373 - val_loss: 213534512.0000 - val_mae: 11679.8955\n",
      "Epoch 562/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 186653104.0000 - mae: 10518.3184 - val_loss: 213764240.0000 - val_mae: 11686.6973\n",
      "Epoch 563/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 186595776.0000 - mae: 10518.1816 - val_loss: 213351904.0000 - val_mae: 11671.3887\n",
      "Epoch 564/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 186451696.0000 - mae: 10516.7998 - val_loss: 213178560.0000 - val_mae: 11662.9346\n",
      "Epoch 565/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 186315600.0000 - mae: 10511.8857 - val_loss: 213152992.0000 - val_mae: 11660.0684\n",
      "Epoch 566/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 186401280.0000 - mae: 10518.0146 - val_loss: 212564864.0000 - val_mae: 11638.3848\n",
      "Epoch 567/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 186108128.0000 - mae: 10513.9707 - val_loss: 212575920.0000 - val_mae: 11638.5244\n",
      "Epoch 568/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 186016432.0000 - mae: 10509.9434 - val_loss: 212598736.0000 - val_mae: 11637.8809\n",
      "Epoch 569/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 185870224.0000 - mae: 10505.7314 - val_loss: 212210768.0000 - val_mae: 11624.8037\n",
      "Epoch 570/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 185758304.0000 - mae: 10506.4561 - val_loss: 212010080.0000 - val_mae: 11616.7197\n",
      "Epoch 571/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 185693104.0000 - mae: 10505.3887 - val_loss: 211912976.0000 - val_mae: 11612.8096\n",
      "Epoch 572/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 185572800.0000 - mae: 10502.9395 - val_loss: 211848944.0000 - val_mae: 11609.6807\n",
      "Epoch 573/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 185571424.0000 - mae: 10505.5977 - val_loss: 211407136.0000 - val_mae: 11591.5693\n",
      "Epoch 574/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 185374016.0000 - mae: 10503.5771 - val_loss: 211233776.0000 - val_mae: 11582.9492\n",
      "Epoch 575/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 185272656.0000 - mae: 10501.1143 - val_loss: 211276640.0000 - val_mae: 11583.3857\n",
      "Epoch 576/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 185155296.0000 - mae: 10495.7803 - val_loss: 211369632.0000 - val_mae: 11586.4414\n",
      "Epoch 577/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 185112032.0000 - mae: 10490.8213 - val_loss: 211481328.0000 - val_mae: 11588.9863\n",
      "Epoch 578/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 184963536.0000 - mae: 10486.0020 - val_loss: 211213072.0000 - val_mae: 11578.8564\n",
      "Epoch 579/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 184868816.0000 - mae: 10485.6309 - val_loss: 210848432.0000 - val_mae: 11564.8975\n",
      "Epoch 580/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 184793456.0000 - mae: 10484.9414 - val_loss: 210550896.0000 - val_mae: 11552.7939\n",
      "Epoch 581/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 184706320.0000 - mae: 10485.1279 - val_loss: 210349920.0000 - val_mae: 11546.3174\n",
      "Epoch 582/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 184618512.0000 - mae: 10485.7227 - val_loss: 210006960.0000 - val_mae: 11532.0244\n",
      "Epoch 583/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 184492496.0000 - mae: 10481.9258 - val_loss: 210070000.0000 - val_mae: 11532.0166\n",
      "Epoch 584/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 184408768.0000 - mae: 10478.2549 - val_loss: 209900144.0000 - val_mae: 11522.4277\n",
      "Epoch 585/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 184477200.0000 - mae: 10483.8799 - val_loss: 209439184.0000 - val_mae: 11504.7686\n",
      "Epoch 586/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 184192448.0000 - mae: 10476.4355 - val_loss: 209751008.0000 - val_mae: 11514.1006\n",
      "Epoch 587/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 184227744.0000 - mae: 10470.6650 - val_loss: 210100160.0000 - val_mae: 11523.9619\n",
      "Epoch 588/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 184305440.0000 - mae: 10475.8906 - val_loss: 209303680.0000 - val_mae: 11498.2441\n",
      "Epoch 589/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 183926048.0000 - mae: 10473.1533 - val_loss: 209191776.0000 - val_mae: 11494.5713\n",
      "Epoch 590/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 183806928.0000 - mae: 10471.8398 - val_loss: 208989552.0000 - val_mae: 11486.1416\n",
      "Epoch 591/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 183663856.0000 - mae: 10468.7549 - val_loss: 208727920.0000 - val_mae: 11476.0420\n",
      "Epoch 592/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 183560848.0000 - mae: 10468.3340 - val_loss: 208338288.0000 - val_mae: 11461.7207\n",
      "Epoch 593/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 183575184.0000 - mae: 10471.0176 - val_loss: 208332464.0000 - val_mae: 11458.8506\n",
      "Epoch 594/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 183453536.0000 - mae: 10469.3281 - val_loss: 207755424.0000 - val_mae: 11439.9600\n",
      "Epoch 595/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 183345328.0000 - mae: 10472.0566 - val_loss: 207412448.0000 - val_mae: 11428.3730\n",
      "Epoch 596/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 183217088.0000 - mae: 10470.0371 - val_loss: 207444368.0000 - val_mae: 11426.2744\n",
      "Epoch 597/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 183073264.0000 - mae: 10463.7148 - val_loss: 207392064.0000 - val_mae: 11422.6738\n",
      "Epoch 598/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 182924864.0000 - mae: 10456.9043 - val_loss: 207605296.0000 - val_mae: 11425.9316\n",
      "Epoch 599/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 182911744.0000 - mae: 10451.4980 - val_loss: 207946784.0000 - val_mae: 11438.2793\n",
      "Epoch 600/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 182782624.0000 - mae: 10445.5635 - val_loss: 207866224.0000 - val_mae: 11432.1621\n",
      "Epoch 601/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 182713488.0000 - mae: 10442.7051 - val_loss: 207743712.0000 - val_mae: 11427.5898\n",
      "Epoch 602/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 182555600.0000 - mae: 10442.2363 - val_loss: 207400960.0000 - val_mae: 11419.3027\n",
      "Epoch 603/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 182419456.0000 - mae: 10442.2500 - val_loss: 206856128.0000 - val_mae: 11402.5723\n",
      "Epoch 604/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 182455152.0000 - mae: 10453.0273 - val_loss: 206284848.0000 - val_mae: 11384.0176\n",
      "Epoch 605/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 182311008.0000 - mae: 10453.2705 - val_loss: 206029968.0000 - val_mae: 11373.0889\n",
      "Epoch 606/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 182190224.0000 - mae: 10443.9111 - val_loss: 206369456.0000 - val_mae: 11379.6729\n",
      "Epoch 607/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 182229952.0000 - mae: 10435.0303 - val_loss: 206706128.0000 - val_mae: 11386.2422\n",
      "Epoch 608/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 181929120.0000 - mae: 10425.6963 - val_loss: 206479488.0000 - val_mae: 11376.0664\n",
      "Epoch 609/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 181785232.0000 - mae: 10424.0000 - val_loss: 206157632.0000 - val_mae: 11364.0674\n",
      "Epoch 610/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 181742672.0000 - mae: 10425.2861 - val_loss: 205621424.0000 - val_mae: 11345.7422\n",
      "Epoch 611/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 181611344.0000 - mae: 10429.9902 - val_loss: 205272656.0000 - val_mae: 11335.3018\n",
      "Epoch 612/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 181546224.0000 - mae: 10432.2451 - val_loss: 205276208.0000 - val_mae: 11336.9609\n",
      "Epoch 613/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 181408768.0000 - mae: 10427.8633 - val_loss: 205228768.0000 - val_mae: 11334.5361\n",
      "Epoch 614/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 181299264.0000 - mae: 10425.4326 - val_loss: 205114960.0000 - val_mae: 11328.3770\n",
      "Epoch 615/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 181209520.0000 - mae: 10423.3721 - val_loss: 205001936.0000 - val_mae: 11323.2129\n",
      "Epoch 616/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 181152288.0000 - mae: 10424.5010 - val_loss: 204448112.0000 - val_mae: 11303.9414\n",
      "Epoch 617/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 181044080.0000 - mae: 10422.7021 - val_loss: 204589856.0000 - val_mae: 11307.1855\n",
      "Epoch 618/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 180826720.0000 - mae: 10415.4062 - val_loss: 204395696.0000 - val_mae: 11299.7510\n",
      "Epoch 619/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 180791584.0000 - mae: 10415.4912 - val_loss: 204239728.0000 - val_mae: 11291.1787\n",
      "Epoch 620/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 180744336.0000 - mae: 10414.5898 - val_loss: 204074928.0000 - val_mae: 11285.6592\n",
      "Epoch 621/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 180639840.0000 - mae: 10412.9580 - val_loss: 204384128.0000 - val_mae: 11293.7324\n",
      "Epoch 622/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 180446816.0000 - mae: 10401.1279 - val_loss: 204380096.0000 - val_mae: 11293.1387\n",
      "Epoch 623/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 180328240.0000 - mae: 10396.3096 - val_loss: 204134368.0000 - val_mae: 11284.9609\n",
      "Epoch 624/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 180144960.0000 - mae: 10395.0908 - val_loss: 203784848.0000 - val_mae: 11272.5732\n",
      "Epoch 625/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 180155296.0000 - mae: 10401.5928 - val_loss: 203198800.0000 - val_mae: 11252.5234\n",
      "Epoch 626/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 180028512.0000 - mae: 10400.5039 - val_loss: 203277504.0000 - val_mae: 11253.5322\n",
      "Epoch 627/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 179922240.0000 - mae: 10395.6426 - val_loss: 203242960.0000 - val_mae: 11247.3389\n",
      "Epoch 628/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 179948704.0000 - mae: 10398.5898 - val_loss: 202679904.0000 - val_mae: 11225.9062\n",
      "Epoch 629/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 179617712.0000 - mae: 10391.6436 - val_loss: 202753360.0000 - val_mae: 11225.4951\n",
      "Epoch 630/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 179484016.0000 - mae: 10382.8701 - val_loss: 202945408.0000 - val_mae: 11228.7168\n",
      "Epoch 631/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 179575776.0000 - mae: 10377.0068 - val_loss: 203527456.0000 - val_mae: 11245.5459\n",
      "Epoch 632/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 179369648.0000 - mae: 10369.4043 - val_loss: 203440064.0000 - val_mae: 11240.0518\n",
      "Epoch 633/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 179278400.0000 - mae: 10369.4795 - val_loss: 203240784.0000 - val_mae: 11233.4795\n",
      "Epoch 634/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 179161264.0000 - mae: 10369.7139 - val_loss: 202742128.0000 - val_mae: 11215.2314\n",
      "Epoch 635/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 178979872.0000 - mae: 10367.6348 - val_loss: 202434192.0000 - val_mae: 11204.0264\n",
      "Epoch 636/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 178901824.0000 - mae: 10367.0732 - val_loss: 202198304.0000 - val_mae: 11196.0928\n",
      "Epoch 637/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 178821680.0000 - mae: 10367.0381 - val_loss: 202066672.0000 - val_mae: 11188.2900\n",
      "Epoch 638/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 178669120.0000 - mae: 10364.6025 - val_loss: 201824080.0000 - val_mae: 11178.8096\n",
      "Epoch 639/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 178735952.0000 - mae: 10370.3096 - val_loss: 201606976.0000 - val_mae: 11171.9580\n",
      "Epoch 640/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 178667424.0000 - mae: 10370.2607 - val_loss: 201188768.0000 - val_mae: 11159.4385\n",
      "Epoch 641/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 178427920.0000 - mae: 10365.8398 - val_loss: 201491648.0000 - val_mae: 11167.0664\n",
      "Epoch 642/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 178258736.0000 - mae: 10355.2979 - val_loss: 201516352.0000 - val_mae: 11166.6416\n",
      "Epoch 643/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 178173184.0000 - mae: 10351.3535 - val_loss: 201707840.0000 - val_mae: 11170.6494\n",
      "Epoch 644/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 178126400.0000 - mae: 10348.2227 - val_loss: 201497968.0000 - val_mae: 11160.2549\n",
      "Epoch 645/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 178039296.0000 - mae: 10346.8975 - val_loss: 201306688.0000 - val_mae: 11150.2871\n",
      "Epoch 646/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 177938192.0000 - mae: 10345.4961 - val_loss: 201288160.0000 - val_mae: 11147.7334\n",
      "Epoch 647/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 177932400.0000 - mae: 10343.2100 - val_loss: 201580320.0000 - val_mae: 11158.7256\n",
      "Epoch 648/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 177745472.0000 - mae: 10337.3135 - val_loss: 201420432.0000 - val_mae: 11152.1875\n",
      "Epoch 649/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 177687344.0000 - mae: 10334.5977 - val_loss: 201325520.0000 - val_mae: 11147.3975\n",
      "Epoch 650/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 177735088.0000 - mae: 10339.1777 - val_loss: 200669456.0000 - val_mae: 11125.3408\n",
      "Epoch 651/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 177547008.0000 - mae: 10338.0498 - val_loss: 200391152.0000 - val_mae: 11114.9600\n",
      "Epoch 652/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 177358096.0000 - mae: 10333.3486 - val_loss: 200394560.0000 - val_mae: 11113.6904\n",
      "Epoch 653/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 177330624.0000 - mae: 10332.9736 - val_loss: 200203472.0000 - val_mae: 11104.2744\n",
      "Epoch 654/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 177166192.0000 - mae: 10329.4336 - val_loss: 199981408.0000 - val_mae: 11096.7666\n",
      "Epoch 655/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 177141056.0000 - mae: 10328.9961 - val_loss: 199824000.0000 - val_mae: 11089.1240\n",
      "Epoch 656/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 177011152.0000 - mae: 10326.5254 - val_loss: 199611664.0000 - val_mae: 11079.4229\n",
      "Epoch 657/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 177034960.0000 - mae: 10330.7812 - val_loss: 199123776.0000 - val_mae: 11060.6006\n",
      "Epoch 658/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 176843504.0000 - mae: 10325.9736 - val_loss: 199089680.0000 - val_mae: 11057.9941\n",
      "Epoch 659/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 176838624.0000 - mae: 10322.3086 - val_loss: 199302544.0000 - val_mae: 11060.0273\n",
      "Epoch 660/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 176725392.0000 - mae: 10315.3564 - val_loss: 199095664.0000 - val_mae: 11049.1709\n",
      "Epoch 661/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 176576880.0000 - mae: 10309.5977 - val_loss: 199119232.0000 - val_mae: 11046.8242\n",
      "Epoch 662/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 176518192.0000 - mae: 10307.2051 - val_loss: 199190976.0000 - val_mae: 11048.3047\n",
      "Epoch 663/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 176460560.0000 - mae: 10307.5605 - val_loss: 198811120.0000 - val_mae: 11031.8115\n",
      "Epoch 664/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 176392880.0000 - mae: 10310.1309 - val_loss: 198515552.0000 - val_mae: 11026.3760\n",
      "Epoch 665/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 176305696.0000 - mae: 10307.3945 - val_loss: 198749088.0000 - val_mae: 11034.0449\n",
      "Epoch 666/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 176240336.0000 - mae: 10305.6475 - val_loss: 198831808.0000 - val_mae: 11039.2939\n",
      "Epoch 667/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 176101856.0000 - mae: 10299.3682 - val_loss: 198562528.0000 - val_mae: 11027.5342\n",
      "Epoch 668/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 176060096.0000 - mae: 10300.8447 - val_loss: 198334384.0000 - val_mae: 11018.6973\n",
      "Epoch 669/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 175934032.0000 - mae: 10301.0322 - val_loss: 198175152.0000 - val_mae: 11009.9004\n",
      "Epoch 670/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 175846208.0000 - mae: 10298.9570 - val_loss: 198162032.0000 - val_mae: 11009.8877\n",
      "Epoch 671/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 175833392.0000 - mae: 10296.5674 - val_loss: 198589360.0000 - val_mae: 11026.3164\n",
      "Epoch 672/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 175772768.0000 - mae: 10289.7646 - val_loss: 198770816.0000 - val_mae: 11031.9092\n",
      "Epoch 673/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 175602240.0000 - mae: 10284.7080 - val_loss: 198535520.0000 - val_mae: 11023.8311\n",
      "Epoch 674/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 175521616.0000 - mae: 10285.5000 - val_loss: 197889776.0000 - val_mae: 11001.9111\n",
      "Epoch 675/1000\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 175456832.0000 - mae: 10292.0020 - val_loss: 197294480.0000 - val_mae: 10980.5986\n",
      "Epoch 676/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 175345856.0000 - mae: 10290.9131 - val_loss: 197419920.0000 - val_mae: 10984.3994\n",
      "Epoch 677/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 175255696.0000 - mae: 10287.0293 - val_loss: 197414112.0000 - val_mae: 10983.8047\n",
      "Epoch 678/1000\n",
      "4/4 [==============================] - ETA: 0s - loss: 129908352.0000 - mae: 8645.60 - 0s 8ms/step - loss: 175077488.0000 - mae: 10280.3057 - val_loss: 197064672.0000 - val_mae: 10969.0400\n",
      "Epoch 679/1000\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 175005968.0000 - mae: 10279.1426 - val_loss: 196694832.0000 - val_mae: 10955.7021\n",
      "Epoch 680/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 175009504.0000 - mae: 10287.5840 - val_loss: 196427584.0000 - val_mae: 10946.9678\n",
      "Epoch 681/1000\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 174891536.0000 - mae: 10285.5840 - val_loss: 196246608.0000 - val_mae: 10941.9092\n",
      "Epoch 682/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 174941456.0000 - mae: 10283.0771 - val_loss: 196585008.0000 - val_mae: 10949.3975\n",
      "Epoch 683/1000\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 174688736.0000 - mae: 10273.1504 - val_loss: 196617376.0000 - val_mae: 10951.0664\n",
      "Epoch 684/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 174615568.0000 - mae: 10271.5850 - val_loss: 196583648.0000 - val_mae: 10950.0820\n",
      "Epoch 685/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 174499872.0000 - mae: 10265.9053 - val_loss: 196714288.0000 - val_mae: 10952.6064\n",
      "Epoch 686/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 174397936.0000 - mae: 10262.6650 - val_loss: 196518080.0000 - val_mae: 10941.4561\n",
      "Epoch 687/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 174322368.0000 - mae: 10260.5908 - val_loss: 196395728.0000 - val_mae: 10937.4121\n",
      "Epoch 688/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 174224672.0000 - mae: 10257.6250 - val_loss: 196370848.0000 - val_mae: 10935.8936\n",
      "Epoch 689/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 174267072.0000 - mae: 10263.6670 - val_loss: 195946704.0000 - val_mae: 10919.8730\n",
      "Epoch 690/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 174053168.0000 - mae: 10257.9775 - val_loss: 196018848.0000 - val_mae: 10920.9941\n",
      "Epoch 691/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 174040896.0000 - mae: 10256.0586 - val_loss: 196189872.0000 - val_mae: 10926.1396\n",
      "Epoch 692/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 173899744.0000 - mae: 10249.1045 - val_loss: 195943760.0000 - val_mae: 10917.3604\n",
      "Epoch 693/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 173975584.0000 - mae: 10248.3477 - val_loss: 196163472.0000 - val_mae: 10923.3584\n",
      "Epoch 694/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 173797680.0000 - mae: 10246.0449 - val_loss: 195634672.0000 - val_mae: 10903.1523\n",
      "Epoch 695/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 173687840.0000 - mae: 10245.6689 - val_loss: 195222768.0000 - val_mae: 10888.4795\n",
      "Epoch 696/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 173578032.0000 - mae: 10248.0244 - val_loss: 195111568.0000 - val_mae: 10882.6846\n",
      "Epoch 697/1000\n",
      "4/4 [==============================] - ETA: 0s - loss: 234122048.0000 - mae: 11402.638 - 0s 4ms/step - loss: 173512704.0000 - mae: 10245.8936 - val_loss: 195113360.0000 - val_mae: 10881.5244\n",
      "Epoch 698/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 173376400.0000 - mae: 10238.4600 - val_loss: 195502384.0000 - val_mae: 10895.7529\n",
      "Epoch 699/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 173316032.0000 - mae: 10232.6826 - val_loss: 195643056.0000 - val_mae: 10903.7676\n",
      "Epoch 700/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 173304800.0000 - mae: 10233.5488 - val_loss: 195491712.0000 - val_mae: 10897.8809\n",
      "Epoch 701/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 173191408.0000 - mae: 10230.7539 - val_loss: 195364496.0000 - val_mae: 10893.0527\n",
      "Epoch 702/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 173112496.0000 - mae: 10229.8076 - val_loss: 195143552.0000 - val_mae: 10882.5420\n",
      "Epoch 703/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 173027488.0000 - mae: 10227.3838 - val_loss: 195086496.0000 - val_mae: 10879.0508\n",
      "Epoch 704/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 172953360.0000 - mae: 10224.7129 - val_loss: 194856768.0000 - val_mae: 10870.2764\n",
      "Epoch 705/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 172863856.0000 - mae: 10222.5576 - val_loss: 194819968.0000 - val_mae: 10869.1777\n",
      "Epoch 706/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 172797264.0000 - mae: 10219.8643 - val_loss: 194770064.0000 - val_mae: 10868.0840\n",
      "Epoch 707/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 172782800.0000 - mae: 10222.3604 - val_loss: 194923568.0000 - val_mae: 10878.9307\n",
      "Epoch 708/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 172741088.0000 - mae: 10222.6904 - val_loss: 194669296.0000 - val_mae: 10871.6973\n",
      "Epoch 709/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 172572640.0000 - mae: 10218.2256 - val_loss: 194726512.0000 - val_mae: 10873.8828\n",
      "Epoch 710/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 172530592.0000 - mae: 10217.2852 - val_loss: 194617680.0000 - val_mae: 10870.8486\n",
      "Epoch 711/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 172412752.0000 - mae: 10214.7646 - val_loss: 194489888.0000 - val_mae: 10865.5596\n",
      "Epoch 712/1000\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 172426208.0000 - mae: 10219.5107 - val_loss: 194180944.0000 - val_mae: 10854.2861\n",
      "Epoch 713/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 172347664.0000 - mae: 10217.2832 - val_loss: 193931040.0000 - val_mae: 10843.0107\n",
      "Epoch 714/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 172250720.0000 - mae: 10215.9492 - val_loss: 193707072.0000 - val_mae: 10836.0420\n",
      "Epoch 715/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 172181024.0000 - mae: 10211.9111 - val_loss: 193804576.0000 - val_mae: 10834.6758\n",
      "Epoch 716/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 172105280.0000 - mae: 10209.2832 - val_loss: 193562080.0000 - val_mae: 10824.2041\n",
      "Epoch 717/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 172036016.0000 - mae: 10203.0566 - val_loss: 193890176.0000 - val_mae: 10835.9941\n",
      "Epoch 718/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 171926896.0000 - mae: 10198.9023 - val_loss: 193874112.0000 - val_mae: 10835.4756\n",
      "Epoch 719/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 171816496.0000 - mae: 10195.9736 - val_loss: 193843392.0000 - val_mae: 10832.9033\n",
      "Epoch 720/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 171751632.0000 - mae: 10194.0518 - val_loss: 193630672.0000 - val_mae: 10824.4316\n",
      "Epoch 721/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 171701088.0000 - mae: 10192.7744 - val_loss: 193507408.0000 - val_mae: 10817.7402\n",
      "Epoch 722/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 171641616.0000 - mae: 10193.0068 - val_loss: 193349360.0000 - val_mae: 10812.9971\n",
      "Epoch 723/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 171633344.0000 - mae: 10193.7051 - val_loss: 193355872.0000 - val_mae: 10810.7441\n",
      "Epoch 724/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 171507504.0000 - mae: 10189.5312 - val_loss: 193365184.0000 - val_mae: 10814.3291\n",
      "Epoch 725/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 171367568.0000 - mae: 10186.7451 - val_loss: 193178832.0000 - val_mae: 10808.3672\n",
      "Epoch 726/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 171321312.0000 - mae: 10188.1357 - val_loss: 193044912.0000 - val_mae: 10807.2793\n",
      "Epoch 727/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 171261872.0000 - mae: 10188.0264 - val_loss: 192884800.0000 - val_mae: 10803.4785\n",
      "Epoch 728/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 171172864.0000 - mae: 10183.3086 - val_loss: 193054688.0000 - val_mae: 10808.3604\n",
      "Epoch 729/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 171122224.0000 - mae: 10179.3867 - val_loss: 192990288.0000 - val_mae: 10805.9316\n",
      "Epoch 730/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 171029328.0000 - mae: 10177.2793 - val_loss: 192847680.0000 - val_mae: 10802.2568\n",
      "Epoch 731/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 171002000.0000 - mae: 10177.5322 - val_loss: 192921008.0000 - val_mae: 10805.5762\n",
      "Epoch 732/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 171019872.0000 - mae: 10183.0664 - val_loss: 192482896.0000 - val_mae: 10790.0293\n",
      "Epoch 733/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 170799792.0000 - mae: 10176.2148 - val_loss: 192528416.0000 - val_mae: 10792.4756\n",
      "Epoch 734/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 170732640.0000 - mae: 10174.1836 - val_loss: 192547264.0000 - val_mae: 10792.4287\n",
      "Epoch 735/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 170642608.0000 - mae: 10169.7949 - val_loss: 192642336.0000 - val_mae: 10794.8555\n",
      "Epoch 736/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 170649120.0000 - mae: 10169.1143 - val_loss: 192753504.0000 - val_mae: 10799.6387\n",
      "Epoch 737/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 170677088.0000 - mae: 10167.9463 - val_loss: 193038912.0000 - val_mae: 10812.7510\n",
      "Epoch 738/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 170437152.0000 - mae: 10159.0352 - val_loss: 192627088.0000 - val_mae: 10794.7881\n",
      "Epoch 739/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 170356688.0000 - mae: 10161.6289 - val_loss: 191974608.0000 - val_mae: 10769.8311\n",
      "Epoch 740/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 170566688.0000 - mae: 10175.7588 - val_loss: 191255520.0000 - val_mae: 10747.2363\n",
      "Epoch 741/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 170280000.0000 - mae: 10171.0605 - val_loss: 191413312.0000 - val_mae: 10750.1123\n",
      "Epoch 742/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 170130800.0000 - mae: 10165.5293 - val_loss: 191670080.0000 - val_mae: 10760.2461\n",
      "Epoch 743/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 170076688.0000 - mae: 10159.0801 - val_loss: 192082880.0000 - val_mae: 10774.8818\n",
      "Epoch 744/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 170204576.0000 - mae: 10155.7637 - val_loss: 192590384.0000 - val_mae: 10797.5059\n",
      "Epoch 745/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 169942448.0000 - mae: 10146.9004 - val_loss: 192167328.0000 - val_mae: 10779.8076\n",
      "Epoch 746/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 169864704.0000 - mae: 10149.6738 - val_loss: 191572192.0000 - val_mae: 10758.2061\n",
      "Epoch 747/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 169807536.0000 - mae: 10157.5225 - val_loss: 191167824.0000 - val_mae: 10747.9541\n",
      "Epoch 748/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 169770256.0000 - mae: 10158.4131 - val_loss: 190857312.0000 - val_mae: 10735.5859\n",
      "Epoch 749/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 169913312.0000 - mae: 10159.2617 - val_loss: 191386288.0000 - val_mae: 10745.5957\n",
      "Epoch 750/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 169601072.0000 - mae: 10145.2090 - val_loss: 191430576.0000 - val_mae: 10746.6484\n",
      "Epoch 751/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 169561648.0000 - mae: 10143.5166 - val_loss: 191528752.0000 - val_mae: 10752.7061\n",
      "Epoch 752/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 169487552.0000 - mae: 10141.0449 - val_loss: 191610800.0000 - val_mae: 10756.2480\n",
      "Epoch 753/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 169422384.0000 - mae: 10137.7139 - val_loss: 191431712.0000 - val_mae: 10747.7891\n",
      "Epoch 754/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 169249040.0000 - mae: 10137.7969 - val_loss: 190720960.0000 - val_mae: 10723.7549\n",
      "Epoch 755/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 169221888.0000 - mae: 10140.9395 - val_loss: 190406464.0000 - val_mae: 10712.4922\n",
      "Epoch 756/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 169205440.0000 - mae: 10143.8174 - val_loss: 190103424.0000 - val_mae: 10699.7314\n",
      "Epoch 757/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 169127632.0000 - mae: 10142.5811 - val_loss: 190122784.0000 - val_mae: 10700.0645\n",
      "Epoch 758/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 169101360.0000 - mae: 10140.2842 - val_loss: 190199616.0000 - val_mae: 10701.1875\n",
      "Epoch 759/1000\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 169099760.0000 - mae: 10134.6885 - val_loss: 190709952.0000 - val_mae: 10713.8389\n",
      "Epoch 760/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 168954816.0000 - mae: 10126.8613 - val_loss: 190918192.0000 - val_mae: 10721.1885\n",
      "Epoch 761/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 168840640.0000 - mae: 10123.1465 - val_loss: 190524192.0000 - val_mae: 10706.6650\n",
      "Epoch 762/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 169046288.0000 - mae: 10131.7881 - val_loss: 190019488.0000 - val_mae: 10691.3066\n",
      "Epoch 763/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 168686256.0000 - mae: 10125.9307 - val_loss: 190134448.0000 - val_mae: 10695.9453\n",
      "Epoch 764/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 168585072.0000 - mae: 10120.0537 - val_loss: 190489136.0000 - val_mae: 10706.7256\n",
      "Epoch 765/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 168673424.0000 - mae: 10115.6455 - val_loss: 190992576.0000 - val_mae: 10726.3203\n",
      "Epoch 766/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 168649424.0000 - mae: 10118.5371 - val_loss: 190460960.0000 - val_mae: 10707.7285\n",
      "Epoch 767/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 168535536.0000 - mae: 10115.7773 - val_loss: 190570448.0000 - val_mae: 10710.6475\n",
      "Epoch 768/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 168369680.0000 - mae: 10110.3350 - val_loss: 190292688.0000 - val_mae: 10701.8584\n",
      "Epoch 769/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 168250176.0000 - mae: 10112.4688 - val_loss: 189661360.0000 - val_mae: 10682.5059\n",
      "Epoch 770/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 168277408.0000 - mae: 10114.3242 - val_loss: 189615616.0000 - val_mae: 10679.7207\n",
      "Epoch 771/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 168161488.0000 - mae: 10112.2217 - val_loss: 189247424.0000 - val_mae: 10671.7539\n",
      "Epoch 772/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 168091040.0000 - mae: 10113.6377 - val_loss: 189041088.0000 - val_mae: 10660.5703\n",
      "Epoch 773/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 168067264.0000 - mae: 10112.0586 - val_loss: 189079536.0000 - val_mae: 10657.3135\n",
      "Epoch 774/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 168006752.0000 - mae: 10109.0273 - val_loss: 188915872.0000 - val_mae: 10652.8262\n",
      "Epoch 775/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 167927600.0000 - mae: 10105.6377 - val_loss: 189185712.0000 - val_mae: 10660.4170\n",
      "Epoch 776/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 167879232.0000 - mae: 10099.6650 - val_loss: 189684400.0000 - val_mae: 10680.2764\n",
      "Epoch 777/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 167770144.0000 - mae: 10095.6445 - val_loss: 189681200.0000 - val_mae: 10677.6436\n",
      "Epoch 778/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 167743792.0000 - mae: 10095.3555 - val_loss: 189570480.0000 - val_mae: 10678.8008\n",
      "Epoch 779/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 167837152.0000 - mae: 10102.5186 - val_loss: 189184160.0000 - val_mae: 10668.9766\n",
      "Epoch 780/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 167639872.0000 - mae: 10094.5459 - val_loss: 189593728.0000 - val_mae: 10683.7168\n",
      "Epoch 781/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 167581888.0000 - mae: 10090.4883 - val_loss: 189754704.0000 - val_mae: 10684.6504\n",
      "Epoch 782/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 167518640.0000 - mae: 10090.3887 - val_loss: 189371680.0000 - val_mae: 10676.2822\n",
      "Epoch 783/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 167385520.0000 - mae: 10085.5869 - val_loss: 189277808.0000 - val_mae: 10671.5186\n",
      "Epoch 784/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 167406912.0000 - mae: 10087.3604 - val_loss: 188790640.0000 - val_mae: 10654.1211\n",
      "Epoch 785/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 167304256.0000 - mae: 10084.1113 - val_loss: 189112096.0000 - val_mae: 10662.5166\n",
      "Epoch 786/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 167192176.0000 - mae: 10080.4258 - val_loss: 189001072.0000 - val_mae: 10659.9463\n",
      "Epoch 787/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 167163808.0000 - mae: 10078.1553 - val_loss: 189131712.0000 - val_mae: 10663.3027\n",
      "Epoch 788/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 167118064.0000 - mae: 10077.4873 - val_loss: 189024720.0000 - val_mae: 10661.0801\n",
      "Epoch 789/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 166986240.0000 - mae: 10077.9248 - val_loss: 188474832.0000 - val_mae: 10641.8193\n",
      "Epoch 790/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 167020352.0000 - mae: 10079.5996 - val_loss: 187880928.0000 - val_mae: 10625.7559\n",
      "Epoch 791/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 166890080.0000 - mae: 10078.5850 - val_loss: 187726000.0000 - val_mae: 10616.0039\n",
      "Epoch 792/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 166908928.0000 - mae: 10080.4619 - val_loss: 187565056.0000 - val_mae: 10604.2725\n",
      "Epoch 793/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 166778224.0000 - mae: 10076.5723 - val_loss: 187592496.0000 - val_mae: 10609.7109\n",
      "Epoch 794/1000\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 166753056.0000 - mae: 10073.1299 - val_loss: 188041328.0000 - val_mae: 10617.8223\n",
      "Epoch 795/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 166591264.0000 - mae: 10065.8350 - val_loss: 188274880.0000 - val_mae: 10626.7803\n",
      "Epoch 796/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 166611920.0000 - mae: 10061.7002 - val_loss: 188567616.0000 - val_mae: 10637.0215\n",
      "Epoch 797/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 166491248.0000 - mae: 10058.4775 - val_loss: 188389872.0000 - val_mae: 10636.0742\n",
      "Epoch 798/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 166635824.0000 - mae: 10073.0947 - val_loss: 187664896.0000 - val_mae: 10616.9697\n",
      "Epoch 799/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 166407104.0000 - mae: 10067.7812 - val_loss: 187892784.0000 - val_mae: 10626.3516\n",
      "Epoch 800/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 166315792.0000 - mae: 10062.3418 - val_loss: 187884608.0000 - val_mae: 10623.9180\n",
      "Epoch 801/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 166278176.0000 - mae: 10062.3008 - val_loss: 187672992.0000 - val_mae: 10615.7949\n",
      "Epoch 802/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 166313120.0000 - mae: 10059.8691 - val_loss: 187933664.0000 - val_mae: 10617.2500\n",
      "Epoch 803/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 166192528.0000 - mae: 10054.1768 - val_loss: 187734976.0000 - val_mae: 10615.5654\n",
      "Epoch 804/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 166048944.0000 - mae: 10052.3223 - val_loss: 187454864.0000 - val_mae: 10606.8408\n",
      "Epoch 805/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 166001168.0000 - mae: 10051.5977 - val_loss: 187392000.0000 - val_mae: 10599.0205\n",
      "Epoch 806/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 165920752.0000 - mae: 10048.3330 - val_loss: 187257952.0000 - val_mae: 10592.2207\n",
      "Epoch 807/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 165932464.0000 - mae: 10046.3027 - val_loss: 187291664.0000 - val_mae: 10589.8506\n",
      "Epoch 808/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 165896352.0000 - mae: 10045.1895 - val_loss: 187071840.0000 - val_mae: 10581.8066\n",
      "Epoch 809/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 165774096.0000 - mae: 10040.3613 - val_loss: 187314512.0000 - val_mae: 10591.7793\n",
      "Epoch 810/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 165715712.0000 - mae: 10039.3711 - val_loss: 187224928.0000 - val_mae: 10594.0928\n",
      "Epoch 811/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 165596176.0000 - mae: 10037.8633 - val_loss: 187250320.0000 - val_mae: 10598.2783\n",
      "Epoch 812/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 165595344.0000 - mae: 10037.2793 - val_loss: 187175504.0000 - val_mae: 10599.8145\n",
      "Epoch 813/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 165518048.0000 - mae: 10035.5762 - val_loss: 187119152.0000 - val_mae: 10597.5947\n",
      "Epoch 814/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 165437952.0000 - mae: 10035.0850 - val_loss: 187107680.0000 - val_mae: 10590.4863\n",
      "Epoch 815/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 165385200.0000 - mae: 10031.3486 - val_loss: 187245184.0000 - val_mae: 10593.7158\n",
      "Epoch 816/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 165358624.0000 - mae: 10025.1133 - val_loss: 187438736.0000 - val_mae: 10598.3184\n",
      "Epoch 817/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 165237856.0000 - mae: 10021.8730 - val_loss: 187046752.0000 - val_mae: 10585.5986\n",
      "Epoch 818/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 165208592.0000 - mae: 10027.2051 - val_loss: 186665424.0000 - val_mae: 10573.5703\n",
      "Epoch 819/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 165314512.0000 - mae: 10038.9912 - val_loss: 186305216.0000 - val_mae: 10564.3164\n",
      "Epoch 820/1000\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 165174832.0000 - mae: 10026.3457 - val_loss: 186810736.0000 - val_mae: 10574.6494\n",
      "Epoch 821/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 165053232.0000 - mae: 10017.9893 - val_loss: 186944592.0000 - val_mae: 10578.1270\n",
      "Epoch 822/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 164957552.0000 - mae: 10012.9258 - val_loss: 186770560.0000 - val_mae: 10570.6836\n",
      "Epoch 823/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 164836160.0000 - mae: 10012.1152 - val_loss: 186109728.0000 - val_mae: 10552.5400\n",
      "Epoch 824/1000\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 164786032.0000 - mae: 10019.6758 - val_loss: 185744896.0000 - val_mae: 10546.5078\n",
      "Epoch 825/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 164752656.0000 - mae: 10023.3340 - val_loss: 185597344.0000 - val_mae: 10542.6592\n",
      "Epoch 826/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 164709056.0000 - mae: 10027.9561 - val_loss: 185403600.0000 - val_mae: 10539.4160\n",
      "Epoch 827/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 164709472.0000 - mae: 10032.9727 - val_loss: 185380848.0000 - val_mae: 10541.0400\n",
      "Epoch 828/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 164685392.0000 - mae: 10027.7705 - val_loss: 185910640.0000 - val_mae: 10556.7520\n",
      "Epoch 829/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 164471872.0000 - mae: 10015.3398 - val_loss: 185851808.0000 - val_mae: 10555.7471\n",
      "Epoch 830/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 164400832.0000 - mae: 10012.6357 - val_loss: 185948352.0000 - val_mae: 10557.9463\n",
      "Epoch 831/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 164413328.0000 - mae: 10012.8018 - val_loss: 185948720.0000 - val_mae: 10558.7090\n",
      "Epoch 832/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 164384400.0000 - mae: 10009.2471 - val_loss: 186452128.0000 - val_mae: 10566.2285\n",
      "Epoch 833/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 164214416.0000 - mae: 9992.7168 - val_loss: 186445632.0000 - val_mae: 10565.2480\n",
      "Epoch 834/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 164182704.0000 - mae: 9992.5459 - val_loss: 186122480.0000 - val_mae: 10552.5527\n",
      "Epoch 835/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 164169392.0000 - mae: 9998.9521 - val_loss: 185607200.0000 - val_mae: 10534.3301\n",
      "Epoch 836/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 164033536.0000 - mae: 9998.0957 - val_loss: 185430208.0000 - val_mae: 10527.4482\n",
      "Epoch 837/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 164150176.0000 - mae: 10006.8477 - val_loss: 185033232.0000 - val_mae: 10522.9336\n",
      "Epoch 838/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 163955024.0000 - mae: 10002.8818 - val_loss: 185024640.0000 - val_mae: 10522.3779\n",
      "Epoch 839/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 163956064.0000 - mae: 10006.6084 - val_loss: 184838752.0000 - val_mae: 10517.0674\n",
      "Epoch 840/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 163813088.0000 - mae: 10001.2812 - val_loss: 185052464.0000 - val_mae: 10520.6768\n",
      "Epoch 841/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 163928928.0000 - mae: 9994.1758 - val_loss: 185770880.0000 - val_mae: 10541.6455\n",
      "Epoch 842/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 163677664.0000 - mae: 9983.7354 - val_loss: 185495824.0000 - val_mae: 10530.4756\n",
      "Epoch 843/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 163606432.0000 - mae: 9982.9102 - val_loss: 185343648.0000 - val_mae: 10521.9756\n",
      "Epoch 844/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 163505776.0000 - mae: 9980.8174 - val_loss: 185099664.0000 - val_mae: 10514.3799\n",
      "Epoch 845/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 163459232.0000 - mae: 9983.5996 - val_loss: 184620624.0000 - val_mae: 10500.7930\n",
      "Epoch 846/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 163453616.0000 - mae: 9990.6670 - val_loss: 184394096.0000 - val_mae: 10495.9639\n",
      "Epoch 847/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 163433456.0000 - mae: 9989.7637 - val_loss: 184588224.0000 - val_mae: 10495.0566\n",
      "Epoch 848/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 163274416.0000 - mae: 9981.8008 - val_loss: 184586800.0000 - val_mae: 10497.7920\n",
      "Epoch 849/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 163265280.0000 - mae: 9978.7842 - val_loss: 184726752.0000 - val_mae: 10498.9336\n",
      "Epoch 850/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 163247088.0000 - mae: 9972.8105 - val_loss: 184897664.0000 - val_mae: 10503.5869\n",
      "Epoch 851/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 163116480.0000 - mae: 9969.6836 - val_loss: 184600048.0000 - val_mae: 10493.6162\n",
      "Epoch 852/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 163073616.0000 - mae: 9973.5977 - val_loss: 184276528.0000 - val_mae: 10485.6289\n",
      "Epoch 853/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 163002272.0000 - mae: 9975.5684 - val_loss: 184335792.0000 - val_mae: 10492.9805\n",
      "Epoch 854/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 162950944.0000 - mae: 9970.9531 - val_loss: 184553472.0000 - val_mae: 10497.8672\n",
      "Epoch 855/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 162863952.0000 - mae: 9966.7646 - val_loss: 184509296.0000 - val_mae: 10496.8271\n",
      "Epoch 856/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 162841840.0000 - mae: 9967.4912 - val_loss: 184274720.0000 - val_mae: 10484.5488\n",
      "Epoch 857/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 162736160.0000 - mae: 9962.4209 - val_loss: 184271008.0000 - val_mae: 10482.5908\n",
      "Epoch 858/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 162755872.0000 - mae: 9960.7305 - val_loss: 184631456.0000 - val_mae: 10498.3447\n",
      "Epoch 859/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 162663136.0000 - mae: 9952.9307 - val_loss: 184584640.0000 - val_mae: 10494.5996\n",
      "Epoch 860/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 162599632.0000 - mae: 9953.4473 - val_loss: 184299056.0000 - val_mae: 10483.6270\n",
      "Epoch 861/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 162490736.0000 - mae: 9956.3604 - val_loss: 183890384.0000 - val_mae: 10471.3086\n",
      "Epoch 862/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 162465680.0000 - mae: 9959.3887 - val_loss: 183528768.0000 - val_mae: 10457.3350\n",
      "Epoch 863/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 162395664.0000 - mae: 9958.1250 - val_loss: 183459920.0000 - val_mae: 10453.2188\n",
      "Epoch 864/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 162355456.0000 - mae: 9953.9326 - val_loss: 183671712.0000 - val_mae: 10455.7539\n",
      "Epoch 865/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 162285120.0000 - mae: 9947.8145 - val_loss: 183689088.0000 - val_mae: 10448.7764\n",
      "Epoch 866/1000\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 162204176.0000 - mae: 9940.4971 - val_loss: 183865776.0000 - val_mae: 10450.0254\n",
      "Epoch 867/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 162310256.0000 - mae: 9937.6338 - val_loss: 184245488.0000 - val_mae: 10458.6465\n",
      "Epoch 868/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 162084288.0000 - mae: 9930.4668 - val_loss: 183833520.0000 - val_mae: 10447.9473\n",
      "Epoch 869/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 162112912.0000 - mae: 9942.8193 - val_loss: 183266528.0000 - val_mae: 10432.9033\n",
      "Epoch 870/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 161969584.0000 - mae: 9942.5225 - val_loss: 183027728.0000 - val_mae: 10424.8408\n",
      "Epoch 871/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 161900192.0000 - mae: 9940.0156 - val_loss: 183003232.0000 - val_mae: 10424.8584\n",
      "Epoch 872/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 161816912.0000 - mae: 9934.0557 - val_loss: 183134624.0000 - val_mae: 10428.7686\n",
      "Epoch 873/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 161892080.0000 - mae: 9930.2559 - val_loss: 183441968.0000 - val_mae: 10434.0508\n",
      "Epoch 874/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 161826208.0000 - mae: 9933.7852 - val_loss: 183048384.0000 - val_mae: 10430.1689\n",
      "Epoch 875/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 161622352.0000 - mae: 9929.3145 - val_loss: 183047872.0000 - val_mae: 10428.1934\n",
      "Epoch 876/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 161596224.0000 - mae: 9924.2178 - val_loss: 183036112.0000 - val_mae: 10422.5068\n",
      "Epoch 877/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 161578560.0000 - mae: 9924.0283 - val_loss: 182660560.0000 - val_mae: 10411.3926\n",
      "Epoch 878/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 161483104.0000 - mae: 9921.9160 - val_loss: 182708864.0000 - val_mae: 10411.9004\n",
      "Epoch 879/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 161442384.0000 - mae: 9922.0020 - val_loss: 182716688.0000 - val_mae: 10416.0342\n",
      "Epoch 880/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 161312960.0000 - mae: 9911.1211 - val_loss: 183252496.0000 - val_mae: 10432.5479\n",
      "Epoch 881/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 161272480.0000 - mae: 9902.7812 - val_loss: 183325824.0000 - val_mae: 10433.3213\n",
      "Epoch 882/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 161263776.0000 - mae: 9900.3809 - val_loss: 183381664.0000 - val_mae: 10433.2842\n",
      "Epoch 883/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 161142768.0000 - mae: 9898.4346 - val_loss: 182961824.0000 - val_mae: 10426.1963\n",
      "Epoch 884/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 161118928.0000 - mae: 9906.7021 - val_loss: 182454960.0000 - val_mae: 10408.8223\n",
      "Epoch 885/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 161008304.0000 - mae: 9909.4385 - val_loss: 182237808.0000 - val_mae: 10402.0078\n",
      "Epoch 886/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 161022496.0000 - mae: 9913.5312 - val_loss: 182086128.0000 - val_mae: 10399.1543\n",
      "Epoch 887/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 160845376.0000 - mae: 9906.5537 - val_loss: 182278176.0000 - val_mae: 10404.0635\n",
      "Epoch 888/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 160776000.0000 - mae: 9898.3096 - val_loss: 182625120.0000 - val_mae: 10406.8145\n",
      "Epoch 889/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 160742384.0000 - mae: 9888.8135 - val_loss: 182957456.0000 - val_mae: 10412.2422\n",
      "Epoch 890/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 160696368.0000 - mae: 9882.1143 - val_loss: 182945568.0000 - val_mae: 10410.1025\n",
      "Epoch 891/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 160709072.0000 - mae: 9879.8887 - val_loss: 182890480.0000 - val_mae: 10404.6680\n",
      "Epoch 892/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 160623520.0000 - mae: 9879.8789 - val_loss: 182455472.0000 - val_mae: 10393.9277\n",
      "Epoch 893/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 160482896.0000 - mae: 9878.1270 - val_loss: 182059824.0000 - val_mae: 10380.7236\n",
      "Epoch 894/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 160479232.0000 - mae: 9885.9121 - val_loss: 181297056.0000 - val_mae: 10361.2676\n",
      "Epoch 895/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 160408752.0000 - mae: 9895.9072 - val_loss: 181267696.0000 - val_mae: 10365.6367\n",
      "Epoch 896/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 160409376.0000 - mae: 9898.5068 - val_loss: 181062528.0000 - val_mae: 10362.0186\n",
      "Epoch 897/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 160366864.0000 - mae: 9899.5059 - val_loss: 181259744.0000 - val_mae: 10364.2275\n",
      "Epoch 898/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 160457120.0000 - mae: 9887.9893 - val_loss: 181989792.0000 - val_mae: 10381.9951\n",
      "Epoch 899/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 160166576.0000 - mae: 9875.3740 - val_loss: 181907008.0000 - val_mae: 10378.0068\n",
      "Epoch 900/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 160084336.0000 - mae: 9873.0977 - val_loss: 181903728.0000 - val_mae: 10381.1289\n",
      "Epoch 901/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 159978736.0000 - mae: 9871.2246 - val_loss: 181881056.0000 - val_mae: 10380.6396\n",
      "Epoch 902/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 159938032.0000 - mae: 9871.9980 - val_loss: 181794784.0000 - val_mae: 10378.7139\n",
      "Epoch 903/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 159984384.0000 - mae: 9871.0488 - val_loss: 182049616.0000 - val_mae: 10381.6738\n",
      "Epoch 904/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 159853760.0000 - mae: 9869.0967 - val_loss: 181596208.0000 - val_mae: 10367.8418\n",
      "Epoch 905/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 159768464.0000 - mae: 9870.0254 - val_loss: 181372880.0000 - val_mae: 10357.8311\n",
      "Epoch 906/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 159665120.0000 - mae: 9870.2139 - val_loss: 181383168.0000 - val_mae: 10358.2461\n",
      "Epoch 907/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 159628816.0000 - mae: 9866.3008 - val_loss: 181718384.0000 - val_mae: 10369.8516\n",
      "Epoch 908/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 159561040.0000 - mae: 9862.6523 - val_loss: 181632352.0000 - val_mae: 10366.5967\n",
      "Epoch 909/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 159555696.0000 - mae: 9860.2627 - val_loss: 181883696.0000 - val_mae: 10376.4062\n",
      "Epoch 910/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 159511744.0000 - mae: 9854.5596 - val_loss: 181999264.0000 - val_mae: 10381.3857\n",
      "Epoch 911/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 159383008.0000 - mae: 9850.3711 - val_loss: 181818704.0000 - val_mae: 10376.1309\n",
      "Epoch 912/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 159469872.0000 - mae: 9861.1924 - val_loss: 181068896.0000 - val_mae: 10353.2334\n",
      "Epoch 913/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 159241392.0000 - mae: 9860.9961 - val_loss: 181187616.0000 - val_mae: 10357.7041\n",
      "Epoch 914/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 159278640.0000 - mae: 9861.2100 - val_loss: 181551728.0000 - val_mae: 10370.6865\n",
      "Epoch 915/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 159105584.0000 - mae: 9851.8994 - val_loss: 181388304.0000 - val_mae: 10366.9150\n",
      "Epoch 916/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 159129392.0000 - mae: 9853.4355 - val_loss: 181281504.0000 - val_mae: 10366.1943\n",
      "Epoch 917/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 159098624.0000 - mae: 9847.1670 - val_loss: 181824752.0000 - val_mae: 10384.2881\n",
      "Epoch 918/1000\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 158938608.0000 - mae: 9838.2803 - val_loss: 181785136.0000 - val_mae: 10384.2188\n",
      "Epoch 919/1000\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 158929680.0000 - mae: 9836.9834 - val_loss: 181849808.0000 - val_mae: 10382.8369\n",
      "Epoch 920/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 158988320.0000 - mae: 9844.9238 - val_loss: 181338336.0000 - val_mae: 10366.4512\n",
      "Epoch 921/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 158724000.0000 - mae: 9840.4014 - val_loss: 181499632.0000 - val_mae: 10371.9854\n",
      "Epoch 922/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 158716736.0000 - mae: 9837.0010 - val_loss: 181621584.0000 - val_mae: 10373.4141\n",
      "Epoch 923/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 158637520.0000 - mae: 9834.9814 - val_loss: 181410416.0000 - val_mae: 10363.1699\n",
      "Epoch 924/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 158595680.0000 - mae: 9836.8682 - val_loss: 181204000.0000 - val_mae: 10353.6934\n",
      "Epoch 925/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 158523072.0000 - mae: 9832.7227 - val_loss: 181463696.0000 - val_mae: 10364.2598\n",
      "Epoch 926/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 158442160.0000 - mae: 9826.9248 - val_loss: 181483328.0000 - val_mae: 10363.4492\n",
      "Epoch 927/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 158403120.0000 - mae: 9823.6436 - val_loss: 181342720.0000 - val_mae: 10354.1660\n",
      "Epoch 928/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 158314096.0000 - mae: 9819.4443 - val_loss: 181335504.0000 - val_mae: 10354.6836\n",
      "Epoch 929/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 158328208.0000 - mae: 9812.6201 - val_loss: 181730064.0000 - val_mae: 10370.4570\n",
      "Epoch 930/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 158221776.0000 - mae: 9805.2451 - val_loss: 181630784.0000 - val_mae: 10363.9834\n",
      "Epoch 931/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 158269808.0000 - mae: 9815.6709 - val_loss: 181051456.0000 - val_mae: 10338.5693\n",
      "Epoch 932/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 158140928.0000 - mae: 9812.9121 - val_loss: 181089152.0000 - val_mae: 10338.3086\n",
      "Epoch 933/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 158126528.0000 - mae: 9815.7559 - val_loss: 180805680.0000 - val_mae: 10332.9629\n",
      "Epoch 934/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 157962080.0000 - mae: 9816.4893 - val_loss: 180722128.0000 - val_mae: 10327.6689\n",
      "Epoch 935/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 157961152.0000 - mae: 9813.8926 - val_loss: 180685760.0000 - val_mae: 10329.2832\n",
      "Epoch 936/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 157830432.0000 - mae: 9808.6406 - val_loss: 180807744.0000 - val_mae: 10332.7109\n",
      "Epoch 937/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 157809440.0000 - mae: 9804.1279 - val_loss: 181073072.0000 - val_mae: 10347.1475\n",
      "Epoch 938/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 157695408.0000 - mae: 9797.5566 - val_loss: 180856608.0000 - val_mae: 10339.1777\n",
      "Epoch 939/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 157692128.0000 - mae: 9801.9199 - val_loss: 180563360.0000 - val_mae: 10326.7139\n",
      "Epoch 940/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 157641760.0000 - mae: 9802.5996 - val_loss: 180604960.0000 - val_mae: 10328.8936\n",
      "Epoch 941/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 157511968.0000 - mae: 9799.4990 - val_loss: 180518544.0000 - val_mae: 10328.7139\n",
      "Epoch 942/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 157585392.0000 - mae: 9809.3281 - val_loss: 180255936.0000 - val_mae: 10322.8789\n",
      "Epoch 943/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 157481840.0000 - mae: 9801.1895 - val_loss: 180670752.0000 - val_mae: 10336.7979\n",
      "Epoch 944/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 157389904.0000 - mae: 9796.5811 - val_loss: 180657136.0000 - val_mae: 10339.6475\n",
      "Epoch 945/1000\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 157371232.0000 - mae: 9794.4844 - val_loss: 180670448.0000 - val_mae: 10343.2041\n",
      "Epoch 946/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 157285744.0000 - mae: 9794.8066 - val_loss: 180681728.0000 - val_mae: 10343.3369\n",
      "Epoch 947/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 157202480.0000 - mae: 9788.9834 - val_loss: 180817024.0000 - val_mae: 10348.9258\n",
      "Epoch 948/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 157177456.0000 - mae: 9784.3438 - val_loss: 180613312.0000 - val_mae: 10339.1611\n",
      "Epoch 949/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 157119440.0000 - mae: 9781.8613 - val_loss: 180734112.0000 - val_mae: 10340.7900\n",
      "Epoch 950/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 157058272.0000 - mae: 9777.4717 - val_loss: 180758976.0000 - val_mae: 10341.7373\n",
      "Epoch 951/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 156999104.0000 - mae: 9777.0459 - val_loss: 180471968.0000 - val_mae: 10327.3262\n",
      "Epoch 952/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 156915616.0000 - mae: 9776.0938 - val_loss: 180361712.0000 - val_mae: 10323.8682\n",
      "Epoch 953/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 156877840.0000 - mae: 9779.9326 - val_loss: 179959136.0000 - val_mae: 10307.7549\n",
      "Epoch 954/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 156880944.0000 - mae: 9788.0195 - val_loss: 179784176.0000 - val_mae: 10303.2588\n",
      "Epoch 955/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 156809136.0000 - mae: 9783.5371 - val_loss: 179899392.0000 - val_mae: 10304.8711\n",
      "Epoch 956/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 156715808.0000 - mae: 9780.6338 - val_loss: 179757008.0000 - val_mae: 10300.0771\n",
      "Epoch 957/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 156715568.0000 - mae: 9780.6270 - val_loss: 179622112.0000 - val_mae: 10291.6445\n",
      "Epoch 958/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 156662336.0000 - mae: 9774.1230 - val_loss: 179913248.0000 - val_mae: 10295.5889\n",
      "Epoch 959/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 156560480.0000 - mae: 9768.3955 - val_loss: 179951136.0000 - val_mae: 10299.9746\n",
      "Epoch 960/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 156474784.0000 - mae: 9763.7715 - val_loss: 180109984.0000 - val_mae: 10309.2197\n",
      "Epoch 961/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 156421456.0000 - mae: 9760.1289 - val_loss: 180122944.0000 - val_mae: 10311.5547\n",
      "Epoch 962/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 156370032.0000 - mae: 9760.3691 - val_loss: 180092576.0000 - val_mae: 10312.1240\n",
      "Epoch 963/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 156407456.0000 - mae: 9762.5371 - val_loss: 180047232.0000 - val_mae: 10313.4824\n",
      "Epoch 964/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 156310608.0000 - mae: 9759.5410 - val_loss: 179827296.0000 - val_mae: 10300.5801\n",
      "Epoch 965/1000\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 156236448.0000 - mae: 9757.0254 - val_loss: 179693904.0000 - val_mae: 10300.3633\n",
      "Epoch 966/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 156148592.0000 - mae: 9760.5508 - val_loss: 179457408.0000 - val_mae: 10293.0049\n",
      "Epoch 967/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 156131728.0000 - mae: 9764.9482 - val_loss: 179159472.0000 - val_mae: 10281.7383\n",
      "Epoch 968/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 156053392.0000 - mae: 9764.4941 - val_loss: 179157520.0000 - val_mae: 10282.0928\n",
      "Epoch 969/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 156034304.0000 - mae: 9763.8164 - val_loss: 179221824.0000 - val_mae: 10279.5312\n",
      "Epoch 970/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 155960224.0000 - mae: 9754.8105 - val_loss: 179609968.0000 - val_mae: 10296.1279\n",
      "Epoch 971/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 155951264.0000 - mae: 9749.4473 - val_loss: 179467888.0000 - val_mae: 10286.5791\n",
      "Epoch 972/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 155800496.0000 - mae: 9739.8477 - val_loss: 179796160.0000 - val_mae: 10299.9912\n",
      "Epoch 973/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 155946160.0000 - mae: 9733.7383 - val_loss: 180200144.0000 - val_mae: 10317.8027\n",
      "Epoch 974/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 155932624.0000 - mae: 9730.5713 - val_loss: 179541792.0000 - val_mae: 10287.8516\n",
      "Epoch 975/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 155728352.0000 - mae: 9738.5801 - val_loss: 179327472.0000 - val_mae: 10285.7080\n",
      "Epoch 976/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 155650832.0000 - mae: 9740.2451 - val_loss: 179245792.0000 - val_mae: 10283.8193\n",
      "Epoch 977/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 155613712.0000 - mae: 9734.0947 - val_loss: 179388752.0000 - val_mae: 10291.5000\n",
      "Epoch 978/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 155562560.0000 - mae: 9728.8096 - val_loss: 179451680.0000 - val_mae: 10294.0508\n",
      "Epoch 979/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 155449632.0000 - mae: 9731.0811 - val_loss: 179178032.0000 - val_mae: 10286.3242\n",
      "Epoch 980/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 155427600.0000 - mae: 9737.7383 - val_loss: 178937104.0000 - val_mae: 10277.8438\n",
      "Epoch 981/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 155427536.0000 - mae: 9736.8965 - val_loss: 179134688.0000 - val_mae: 10287.2529\n",
      "Epoch 982/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 155274240.0000 - mae: 9729.7100 - val_loss: 179075168.0000 - val_mae: 10280.9121\n",
      "Epoch 983/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 155502096.0000 - mae: 9730.2949 - val_loss: 179249904.0000 - val_mae: 10285.3691\n",
      "Epoch 984/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 155114224.0000 - mae: 9720.9395 - val_loss: 178642976.0000 - val_mae: 10263.9424\n",
      "Epoch 985/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 155248304.0000 - mae: 9736.5811 - val_loss: 178056304.0000 - val_mae: 10243.0244\n",
      "Epoch 986/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 155196640.0000 - mae: 9739.8066 - val_loss: 178249664.0000 - val_mae: 10246.4414\n",
      "Epoch 987/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 155082160.0000 - mae: 9732.5352 - val_loss: 178198768.0000 - val_mae: 10246.8379\n",
      "Epoch 988/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 155074928.0000 - mae: 9732.8643 - val_loss: 178097440.0000 - val_mae: 10244.4453\n",
      "Epoch 989/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 155038640.0000 - mae: 9720.2822 - val_loss: 178819808.0000 - val_mae: 10270.8887\n",
      "Epoch 990/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 154832224.0000 - mae: 9703.9385 - val_loss: 179016784.0000 - val_mae: 10276.0586\n",
      "Epoch 991/1000\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 154844400.0000 - mae: 9700.0605 - val_loss: 178865136.0000 - val_mae: 10272.5205\n",
      "Epoch 992/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 154724464.0000 - mae: 9698.9775 - val_loss: 178692224.0000 - val_mae: 10262.8086\n",
      "Epoch 993/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 154778080.0000 - mae: 9702.3779 - val_loss: 178356112.0000 - val_mae: 10248.8643\n",
      "Epoch 994/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 154616048.0000 - mae: 9702.3740 - val_loss: 178407568.0000 - val_mae: 10254.1768\n",
      "Epoch 995/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 154573488.0000 - mae: 9696.9248 - val_loss: 178416864.0000 - val_mae: 10254.1270\n",
      "Epoch 996/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 154480240.0000 - mae: 9697.6309 - val_loss: 178207984.0000 - val_mae: 10248.7021\n",
      "Epoch 997/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 154420800.0000 - mae: 9698.9629 - val_loss: 178057520.0000 - val_mae: 10244.7158\n",
      "Epoch 998/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 154377936.0000 - mae: 9699.8779 - val_loss: 178037168.0000 - val_mae: 10244.0439\n",
      "Epoch 999/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 154290176.0000 - mae: 9699.4102 - val_loss: 177840224.0000 - val_mae: 10235.5635\n",
      "Epoch 1000/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 154285360.0000 - mae: 9696.9639 - val_loss: 177880048.0000 - val_mae: 10237.1064\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x2387791b760>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.models import Model\n",
    "demand_model= Model(inputs=inputs,outputs=predictions)\n",
    "demand_model.compile(loss='mse', optimizer='adam', metrics=['mae'])\n",
    "\n",
    "# demand_model.fit(X_train_t,Y_训练集, epochs=7000, validation_split=0.2)\n",
    "demand_model.fit(X_train_t,Y_训练集, epochs=1000, validation_split=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5ff7fbbb-c856-4043-9479-ea915fc8b796",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[63690.094, 64200.562, 64806.53 , 64711.625],\n",
       "       [48033.617, 48418.598, 48875.582, 48803.996],\n",
       "       [81131.516, 81781.77 , 82553.71 , 82432.82 ],\n",
       "       [88528.01 , 89237.555, 90079.87 , 89947.97 ],\n",
       "       [48438.94 , 48827.168, 49288.01 , 49215.82 ],\n",
       "       [52929.387, 53353.605, 53857.18 , 53778.3  ],\n",
       "       [41333.605, 41664.883, 42058.113, 41996.508],\n",
       "       [52834.   , 53257.457, 53760.12 , 53681.383],\n",
       "       [50734.93 , 51141.562, 51624.254, 51548.645],\n",
       "       [65139.66 , 65661.75 , 66281.51 , 66184.445],\n",
       "       [49774.844, 50173.777, 50647.332, 50573.152],\n",
       "       [56598.082, 57051.707, 57590.19 , 57505.848],\n",
       "       [58087.734, 58553.3  , 59105.957, 59019.4  ],\n",
       "       [72847.945, 73431.82 , 74124.93 , 74016.38 ],\n",
       "       [58751.402, 59222.29 , 59781.26 , 59693.71 ],\n",
       "       [52536.23 , 52957.297, 53457.13 , 53378.836],\n",
       "       [55260.61 , 55703.51 , 56229.27 , 56146.918],\n",
       "       [61374.55 , 61866.46 , 62450.395, 62358.938],\n",
       "       [44035.184, 44388.113, 44807.05 , 44741.42 ],\n",
       "       [67169.67 , 67708.03 , 68347.1  , 68247.016],\n",
       "       [80814.54 , 81462.26 , 82231.17 , 82110.766],\n",
       "       [65052.15 , 65573.54 , 66192.46 , 66095.52 ],\n",
       "       [59033.277, 59506.418, 60068.074, 59980.105],\n",
       "       [91891.69 , 92628.195, 93502.516, 93365.6  ],\n",
       "       [53115.8  , 53541.51 , 54046.86 , 53967.703],\n",
       "       [60181.39 , 60663.74 , 61236.32 , 61146.637],\n",
       "       [54670.223, 55108.4  , 55628.535, 55547.062],\n",
       "       [63315.65 , 63823.117, 64425.52 , 64331.17 ],\n",
       "       [52806.58 , 53229.812, 53732.215, 53653.52 ],\n",
       "       [68382.1  , 68930.17 , 69580.79 , 69478.89 ],\n",
       "       [49323.965, 49719.285, 50188.55 , 50115.043],\n",
       "       [61568.824, 62062.29 , 62648.074, 62556.324],\n",
       "       [77418.88 , 78039.39 , 78776.   , 78660.64 ],\n",
       "       [71815.43 , 72391.02 , 73074.305, 72967.3  ],\n",
       "       [80262.67 , 80905.97 , 81669.63 , 81550.05 ],\n",
       "       [62968.156, 63472.84 , 64071.938, 63978.11 ],\n",
       "       [55796.26 , 56243.46 , 56774.312, 56691.164],\n",
       "       [66313.51 , 66845.   , 67475.93 , 67377.12 ]], dtype=float32)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred = demand_model.predict(X_test_t)\n",
    "Y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3738dd8e",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 步骤6：测试模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "75673937-8ea1-486e-bde3-75256ed49a0e",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>75456.11200</td>\n",
       "      <td>61883.92900</td>\n",
       "      <td>58875.84300</td>\n",
       "      <td>68652.54200</td>\n",
       "      <td>63690.093750</td>\n",
       "      <td>64200.562500</td>\n",
       "      <td>64806.531250</td>\n",
       "      <td>64711.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>101387.93600</td>\n",
       "      <td>89558.21300</td>\n",
       "      <td>82059.79600</td>\n",
       "      <td>68921.57300</td>\n",
       "      <td>48033.617188</td>\n",
       "      <td>48418.597656</td>\n",
       "      <td>48875.582031</td>\n",
       "      <td>48803.996094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44073.76800</td>\n",
       "      <td>44767.96600</td>\n",
       "      <td>64112.37700</td>\n",
       "      <td>88478.11000</td>\n",
       "      <td>81131.515625</td>\n",
       "      <td>81781.773438</td>\n",
       "      <td>82553.710938</td>\n",
       "      <td>82432.820312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>71836.55400</td>\n",
       "      <td>78969.65400</td>\n",
       "      <td>64331.20600</td>\n",
       "      <td>63460.57600</td>\n",
       "      <td>88528.007812</td>\n",
       "      <td>89237.554688</td>\n",
       "      <td>90079.867188</td>\n",
       "      <td>89947.968750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49661.08100</td>\n",
       "      <td>51598.04200</td>\n",
       "      <td>46106.97900</td>\n",
       "      <td>87393.47900</td>\n",
       "      <td>48438.941406</td>\n",
       "      <td>48827.167969</td>\n",
       "      <td>49288.011719</td>\n",
       "      <td>49215.820312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>74486.23600</td>\n",
       "      <td>66134.97700</td>\n",
       "      <td>59822.34400</td>\n",
       "      <td>55354.51200</td>\n",
       "      <td>52929.386719</td>\n",
       "      <td>53353.605469</td>\n",
       "      <td>53857.179688</td>\n",
       "      <td>53778.300781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>47896.94495</td>\n",
       "      <td>44238.58208</td>\n",
       "      <td>46817.56708</td>\n",
       "      <td>48796.76647</td>\n",
       "      <td>41333.605469</td>\n",
       "      <td>41664.882812</td>\n",
       "      <td>42058.113281</td>\n",
       "      <td>41996.507812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>52770.41814</td>\n",
       "      <td>53788.79565</td>\n",
       "      <td>60294.52118</td>\n",
       "      <td>56992.28362</td>\n",
       "      <td>52834.000000</td>\n",
       "      <td>53257.457031</td>\n",
       "      <td>53760.121094</td>\n",
       "      <td>53681.382812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>52340.72400</td>\n",
       "      <td>49607.86900</td>\n",
       "      <td>50513.03800</td>\n",
       "      <td>55662.61200</td>\n",
       "      <td>50734.929688</td>\n",
       "      <td>51141.562500</td>\n",
       "      <td>51624.253906</td>\n",
       "      <td>51548.644531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>81851.38300</td>\n",
       "      <td>74231.75200</td>\n",
       "      <td>68665.84100</td>\n",
       "      <td>51326.54000</td>\n",
       "      <td>65139.660156</td>\n",
       "      <td>65661.750000</td>\n",
       "      <td>66281.507812</td>\n",
       "      <td>66184.445312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>58980.16800</td>\n",
       "      <td>56546.02200</td>\n",
       "      <td>57045.68200</td>\n",
       "      <td>56205.43700</td>\n",
       "      <td>49774.843750</td>\n",
       "      <td>50173.777344</td>\n",
       "      <td>50647.332031</td>\n",
       "      <td>50573.152344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>48998.22100</td>\n",
       "      <td>54108.36500</td>\n",
       "      <td>51157.47000</td>\n",
       "      <td>37508.31700</td>\n",
       "      <td>56598.082031</td>\n",
       "      <td>57051.707031</td>\n",
       "      <td>57590.191406</td>\n",
       "      <td>57505.847656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>60771.08901</td>\n",
       "      <td>64967.41426</td>\n",
       "      <td>58108.16900</td>\n",
       "      <td>52340.72400</td>\n",
       "      <td>58087.734375</td>\n",
       "      <td>58553.300781</td>\n",
       "      <td>59105.957031</td>\n",
       "      <td>59019.398438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>50513.03800</td>\n",
       "      <td>55662.61200</td>\n",
       "      <td>68901.79200</td>\n",
       "      <td>94285.70100</td>\n",
       "      <td>72847.945312</td>\n",
       "      <td>73431.820312</td>\n",
       "      <td>74124.929688</td>\n",
       "      <td>74016.382812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>48698.10800</td>\n",
       "      <td>87492.16900</td>\n",
       "      <td>81851.38300</td>\n",
       "      <td>74231.75200</td>\n",
       "      <td>58751.402344</td>\n",
       "      <td>59222.289062</td>\n",
       "      <td>59781.261719</td>\n",
       "      <td>59693.710938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>59822.34400</td>\n",
       "      <td>55354.51200</td>\n",
       "      <td>49661.08100</td>\n",
       "      <td>51598.04200</td>\n",
       "      <td>52536.230469</td>\n",
       "      <td>52957.296875</td>\n",
       "      <td>53457.128906</td>\n",
       "      <td>53378.835938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>66583.14400</td>\n",
       "      <td>60736.82500</td>\n",
       "      <td>65508.91900</td>\n",
       "      <td>54720.87800</td>\n",
       "      <td>55260.609375</td>\n",
       "      <td>55703.511719</td>\n",
       "      <td>56229.269531</td>\n",
       "      <td>56146.917969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>82272.81129</td>\n",
       "      <td>74886.21128</td>\n",
       "      <td>66357.20312</td>\n",
       "      <td>60771.08901</td>\n",
       "      <td>61374.550781</td>\n",
       "      <td>61866.460938</td>\n",
       "      <td>62450.394531</td>\n",
       "      <td>62358.937500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>45266.09349</td>\n",
       "      <td>47896.94495</td>\n",
       "      <td>44238.58208</td>\n",
       "      <td>46817.56708</td>\n",
       "      <td>44035.183594</td>\n",
       "      <td>44388.113281</td>\n",
       "      <td>44807.050781</td>\n",
       "      <td>44741.421875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>80285.05300</td>\n",
       "      <td>82009.47100</td>\n",
       "      <td>86626.26400</td>\n",
       "      <td>85776.38100</td>\n",
       "      <td>67169.671875</td>\n",
       "      <td>67708.031250</td>\n",
       "      <td>68347.101562</td>\n",
       "      <td>68247.015625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>88849.75300</td>\n",
       "      <td>87498.43000</td>\n",
       "      <td>83347.30700</td>\n",
       "      <td>59143.70600</td>\n",
       "      <td>80814.539062</td>\n",
       "      <td>81462.257812</td>\n",
       "      <td>82231.171875</td>\n",
       "      <td>82110.765625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>87498.43000</td>\n",
       "      <td>83347.30700</td>\n",
       "      <td>59143.70600</td>\n",
       "      <td>62996.46400</td>\n",
       "      <td>65052.148438</td>\n",
       "      <td>65573.539062</td>\n",
       "      <td>66192.460938</td>\n",
       "      <td>66095.523438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>49345.90900</td>\n",
       "      <td>67120.71500</td>\n",
       "      <td>78072.01500</td>\n",
       "      <td>62609.11800</td>\n",
       "      <td>59033.277344</td>\n",
       "      <td>59506.417969</td>\n",
       "      <td>60068.074219</td>\n",
       "      <td>59980.105469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>82591.90300</td>\n",
       "      <td>83178.84400</td>\n",
       "      <td>83419.95900</td>\n",
       "      <td>69868.95500</td>\n",
       "      <td>91891.687500</td>\n",
       "      <td>92628.195312</td>\n",
       "      <td>93502.515625</td>\n",
       "      <td>93365.601562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>84330.17657</td>\n",
       "      <td>81551.34279</td>\n",
       "      <td>77111.85826</td>\n",
       "      <td>65163.11274</td>\n",
       "      <td>53115.800781</td>\n",
       "      <td>53541.511719</td>\n",
       "      <td>54046.859375</td>\n",
       "      <td>53967.703125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>63515.25500</td>\n",
       "      <td>72605.64000</td>\n",
       "      <td>66583.14400</td>\n",
       "      <td>60736.82500</td>\n",
       "      <td>60181.390625</td>\n",
       "      <td>60663.738281</td>\n",
       "      <td>61236.320312</td>\n",
       "      <td>61146.636719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27416.91700</td>\n",
       "      <td>35394.17000</td>\n",
       "      <td>49345.90900</td>\n",
       "      <td>67120.71500</td>\n",
       "      <td>54670.222656</td>\n",
       "      <td>55108.398438</td>\n",
       "      <td>55628.535156</td>\n",
       "      <td>55547.062500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>69752.19700</td>\n",
       "      <td>76902.35600</td>\n",
       "      <td>64943.74900</td>\n",
       "      <td>60297.42800</td>\n",
       "      <td>63315.648438</td>\n",
       "      <td>63823.117188</td>\n",
       "      <td>64425.519531</td>\n",
       "      <td>64331.171875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>63460.57600</td>\n",
       "      <td>48998.22100</td>\n",
       "      <td>54108.36500</td>\n",
       "      <td>51157.47000</td>\n",
       "      <td>52806.578125</td>\n",
       "      <td>53229.812500</td>\n",
       "      <td>53732.214844</td>\n",
       "      <td>53653.519531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>59143.70600</td>\n",
       "      <td>62996.46400</td>\n",
       "      <td>60932.08500</td>\n",
       "      <td>48598.88100</td>\n",
       "      <td>68382.101562</td>\n",
       "      <td>68930.171875</td>\n",
       "      <td>69580.789062</td>\n",
       "      <td>69478.890625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>54455.08686</td>\n",
       "      <td>55973.38929</td>\n",
       "      <td>58766.55159</td>\n",
       "      <td>49457.09781</td>\n",
       "      <td>49323.964844</td>\n",
       "      <td>49719.285156</td>\n",
       "      <td>50188.550781</td>\n",
       "      <td>50115.042969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>76273.90600</td>\n",
       "      <td>75456.11200</td>\n",
       "      <td>61883.92900</td>\n",
       "      <td>58875.84300</td>\n",
       "      <td>61568.824219</td>\n",
       "      <td>62062.289062</td>\n",
       "      <td>62648.074219</td>\n",
       "      <td>62556.324219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>37084.70900</td>\n",
       "      <td>64050.87800</td>\n",
       "      <td>76274.58000</td>\n",
       "      <td>69752.19700</td>\n",
       "      <td>77418.882812</td>\n",
       "      <td>78039.390625</td>\n",
       "      <td>78776.000000</td>\n",
       "      <td>78660.640625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>56205.43700</td>\n",
       "      <td>80285.05300</td>\n",
       "      <td>82009.47100</td>\n",
       "      <td>86626.26400</td>\n",
       "      <td>71815.429688</td>\n",
       "      <td>72391.023438</td>\n",
       "      <td>73074.304688</td>\n",
       "      <td>72967.296875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>78969.65400</td>\n",
       "      <td>64331.20600</td>\n",
       "      <td>63460.57600</td>\n",
       "      <td>48998.22100</td>\n",
       "      <td>80262.671875</td>\n",
       "      <td>80905.968750</td>\n",
       "      <td>81669.632812</td>\n",
       "      <td>81550.046875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>43189.54400</td>\n",
       "      <td>40836.79000</td>\n",
       "      <td>48698.10800</td>\n",
       "      <td>87492.16900</td>\n",
       "      <td>62968.156250</td>\n",
       "      <td>63472.839844</td>\n",
       "      <td>64071.937500</td>\n",
       "      <td>63978.109375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>74886.21128</td>\n",
       "      <td>66357.20312</td>\n",
       "      <td>60771.08901</td>\n",
       "      <td>64967.41426</td>\n",
       "      <td>55796.261719</td>\n",
       "      <td>56243.460938</td>\n",
       "      <td>56774.312500</td>\n",
       "      <td>56691.164062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>69765.83800</td>\n",
       "      <td>76273.90600</td>\n",
       "      <td>75456.11200</td>\n",
       "      <td>61883.92900</td>\n",
       "      <td>66313.507812</td>\n",
       "      <td>66845.000000</td>\n",
       "      <td>67475.929688</td>\n",
       "      <td>67377.117188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               0            1            2            3             0  \\\n",
       "0    75456.11200  61883.92900  58875.84300  68652.54200  63690.093750   \n",
       "1   101387.93600  89558.21300  82059.79600  68921.57300  48033.617188   \n",
       "2    44073.76800  44767.96600  64112.37700  88478.11000  81131.515625   \n",
       "3    71836.55400  78969.65400  64331.20600  63460.57600  88528.007812   \n",
       "4    49661.08100  51598.04200  46106.97900  87393.47900  48438.941406   \n",
       "5    74486.23600  66134.97700  59822.34400  55354.51200  52929.386719   \n",
       "6    47896.94495  44238.58208  46817.56708  48796.76647  41333.605469   \n",
       "7    52770.41814  53788.79565  60294.52118  56992.28362  52834.000000   \n",
       "8    52340.72400  49607.86900  50513.03800  55662.61200  50734.929688   \n",
       "9    81851.38300  74231.75200  68665.84100  51326.54000  65139.660156   \n",
       "10   58980.16800  56546.02200  57045.68200  56205.43700  49774.843750   \n",
       "11   48998.22100  54108.36500  51157.47000  37508.31700  56598.082031   \n",
       "12   60771.08901  64967.41426  58108.16900  52340.72400  58087.734375   \n",
       "13   50513.03800  55662.61200  68901.79200  94285.70100  72847.945312   \n",
       "14   48698.10800  87492.16900  81851.38300  74231.75200  58751.402344   \n",
       "15   59822.34400  55354.51200  49661.08100  51598.04200  52536.230469   \n",
       "16   66583.14400  60736.82500  65508.91900  54720.87800  55260.609375   \n",
       "17   82272.81129  74886.21128  66357.20312  60771.08901  61374.550781   \n",
       "18   45266.09349  47896.94495  44238.58208  46817.56708  44035.183594   \n",
       "19   80285.05300  82009.47100  86626.26400  85776.38100  67169.671875   \n",
       "20   88849.75300  87498.43000  83347.30700  59143.70600  80814.539062   \n",
       "21   87498.43000  83347.30700  59143.70600  62996.46400  65052.148438   \n",
       "22   49345.90900  67120.71500  78072.01500  62609.11800  59033.277344   \n",
       "23   82591.90300  83178.84400  83419.95900  69868.95500  91891.687500   \n",
       "24   84330.17657  81551.34279  77111.85826  65163.11274  53115.800781   \n",
       "25   63515.25500  72605.64000  66583.14400  60736.82500  60181.390625   \n",
       "26   27416.91700  35394.17000  49345.90900  67120.71500  54670.222656   \n",
       "27   69752.19700  76902.35600  64943.74900  60297.42800  63315.648438   \n",
       "28   63460.57600  48998.22100  54108.36500  51157.47000  52806.578125   \n",
       "29   59143.70600  62996.46400  60932.08500  48598.88100  68382.101562   \n",
       "30   54455.08686  55973.38929  58766.55159  49457.09781  49323.964844   \n",
       "31   76273.90600  75456.11200  61883.92900  58875.84300  61568.824219   \n",
       "32   37084.70900  64050.87800  76274.58000  69752.19700  77418.882812   \n",
       "33   56205.43700  80285.05300  82009.47100  86626.26400  71815.429688   \n",
       "34   78969.65400  64331.20600  63460.57600  48998.22100  80262.671875   \n",
       "35   43189.54400  40836.79000  48698.10800  87492.16900  62968.156250   \n",
       "36   74886.21128  66357.20312  60771.08901  64967.41426  55796.261719   \n",
       "37   69765.83800  76273.90600  75456.11200  61883.92900  66313.507812   \n",
       "\n",
       "               1             2             3  \n",
       "0   64200.562500  64806.531250  64711.625000  \n",
       "1   48418.597656  48875.582031  48803.996094  \n",
       "2   81781.773438  82553.710938  82432.820312  \n",
       "3   89237.554688  90079.867188  89947.968750  \n",
       "4   48827.167969  49288.011719  49215.820312  \n",
       "5   53353.605469  53857.179688  53778.300781  \n",
       "6   41664.882812  42058.113281  41996.507812  \n",
       "7   53257.457031  53760.121094  53681.382812  \n",
       "8   51141.562500  51624.253906  51548.644531  \n",
       "9   65661.750000  66281.507812  66184.445312  \n",
       "10  50173.777344  50647.332031  50573.152344  \n",
       "11  57051.707031  57590.191406  57505.847656  \n",
       "12  58553.300781  59105.957031  59019.398438  \n",
       "13  73431.820312  74124.929688  74016.382812  \n",
       "14  59222.289062  59781.261719  59693.710938  \n",
       "15  52957.296875  53457.128906  53378.835938  \n",
       "16  55703.511719  56229.269531  56146.917969  \n",
       "17  61866.460938  62450.394531  62358.937500  \n",
       "18  44388.113281  44807.050781  44741.421875  \n",
       "19  67708.031250  68347.101562  68247.015625  \n",
       "20  81462.257812  82231.171875  82110.765625  \n",
       "21  65573.539062  66192.460938  66095.523438  \n",
       "22  59506.417969  60068.074219  59980.105469  \n",
       "23  92628.195312  93502.515625  93365.601562  \n",
       "24  53541.511719  54046.859375  53967.703125  \n",
       "25  60663.738281  61236.320312  61146.636719  \n",
       "26  55108.398438  55628.535156  55547.062500  \n",
       "27  63823.117188  64425.519531  64331.171875  \n",
       "28  53229.812500  53732.214844  53653.519531  \n",
       "29  68930.171875  69580.789062  69478.890625  \n",
       "30  49719.285156  50188.550781  50115.042969  \n",
       "31  62062.289062  62648.074219  62556.324219  \n",
       "32  78039.390625  78776.000000  78660.640625  \n",
       "33  72391.023438  73074.304688  72967.296875  \n",
       "34  80905.968750  81669.632812  81550.046875  \n",
       "35  63472.839844  64071.937500  63978.109375  \n",
       "36  56243.460938  56774.312500  56691.164062  \n",
       "37  66845.000000  67475.929688  67377.117188  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd_Y_test = pd.DataFrame(Y_测试集)\n",
    "pd_Y_pred = pd.DataFrame(Y_pred)\n",
    "data = pd.concat([pd_Y_test,pd_Y_pred], axis=1)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36efb853",
   "metadata": {},
   "source": [
    "## 步骤7：将测试结果可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "270821bc-20ac-405d-bfe2-d212338e1ecc",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd_Y_test = pd.DataFrame(Y_test)\n",
    "pd_Y_pred = pd.DataFrame(Y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15e3d48d",
   "metadata": {},
   "source": [
    "## 步骤8：生成用于生产环境的模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "306e214f-5c91-49a0-ba2a-2704a03ad928",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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